Overview

Dataset statistics

Number of variables73
Number of observations301223
Missing cells2496925
Missing cells (%)11.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory764.1 MiB
Average record size in memory2.6 KiB

Variable types

Numeric11
Categorical57
Unsupported5

Alerts

APO_TRASF has constant value "0.0" Constant
FEC_REP has a high cardinality: 2192 distinct values High cardinality
EDO_DET_parsed has a high cardinality: 51 distinct values High cardinality
MUN_NAC_parsed has a high cardinality: 2164 distinct values High cardinality
MUN_DES_parsed has a high cardinality: 2288 distinct values High cardinality
EN_NAC is highly correlated with MUN_DES and 3 other fieldsHigh correlation
ACOM_REP is highly correlated with EDO_DET and 4 other fieldsHigh correlation
EN_DES is highly correlated with ACOM_REP and 4 other fieldsHigh correlation
MUN_DES is highly correlated with Unnamed: 0 and 4 other fieldsHigh correlation
CAN_AL is highly correlated with CAN_COM and 17 other fieldsHigh correlation
CAN_COM is highly correlated with CAN_AL and 18 other fieldsHigh correlation
CAN_DIF is highly correlated with EDA and 2 other fieldsHigh correlation
CAN_HOS is highly correlated with CAN_STRA and 18 other fieldsHigh correlation
CAN_STRA is highly correlated with Unnamed: 0 and 27 other fieldsHigh correlation
CAN_SEGPOP is highly correlated with Unnamed: 0 and 27 other fieldsHigh correlation
CAN_OFAM is highly correlated with Unnamed: 0 and 27 other fieldsHigh correlation
APO_AUX is highly correlated with Unnamed: 0 and 33 other fieldsHigh correlation
APO_LLAM is highly correlated with DEL and 21 other fieldsHigh correlation
APO_MAT is highly correlated with DEL and 22 other fieldsHigh correlation
APO_TRAS is highly correlated with CAN_STRA and 22 other fieldsHigh correlation
APO_VES is highly correlated with Unnamed: 0 and 31 other fieldsHigh correlation
APO_ACT_NAC is highly correlated with Unnamed: 0 and 29 other fieldsHigh correlation
APO_CURP is highly correlated with CAN_HOS and 30 other fieldsHigh correlation
APO_ASF is highly correlated with Unnamed: 0 and 25 other fieldsHigh correlation
APO_AME is highly correlated with Unnamed: 0 and 27 other fieldsHigh correlation
APO_REC_PERT is highly correlated with Unnamed: 0 and 35 other fieldsHigh correlation
APO_AT_MEDICA is highly correlated with DEL and 23 other fieldsHigh correlation
MUN_NAC is highly correlated with Unnamed: 0 and 4 other fieldsHigh correlation
DEL is highly correlated with CAN_SEGPOP and 8 other fieldsHigh correlation
EDO_DET is highly correlated with ACOM_REP and 4 other fieldsHigh correlation
Unnamed: 0 is highly correlated with EDA and 22 other fieldsHigh correlation
CLASIF_REP is highly correlated with CLASIF_REP_parsedHigh correlation
SEXO is highly correlated with SEXO_parsedHigh correlation
EDA is highly correlated with Unnamed: 0 and 3 other fieldsHigh correlation
NIV_ESC is highly correlated with NIV_ESC_parsedHigh correlation
PERM_EU is highly correlated with PERM_EU_parsedHigh correlation
AUT_DEP is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
AGUA_AL is highly correlated with CAN_AL and 7 other fieldsHigh correlation
DESC_BUS is highly correlated with APO_AUX and 10 other fieldsHigh correlation
DEL_parsed is highly correlated with DEL and 8 other fieldsHigh correlation
CLASIF_REP_parsed is highly correlated with CLASIF_REPHigh correlation
SEXO_parsed is highly correlated with SEXOHigh correlation
EN_NAC_parsed is highly correlated with EN_NAC and 3 other fieldsHigh correlation
NIV_ESC_parsed is highly correlated with NIV_ESCHigh correlation
ACOM_REP_parsed is highly correlated with EDA and 7 other fieldsHigh correlation
PERM_EU_parsed is highly correlated with PERM_EUHigh correlation
EDO_DET_parsed is highly correlated with ACOM_REP and 9 other fieldsHigh correlation
AUT_DEP_parsed is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
EN_DES_parsed is highly correlated with EN_NAC and 9 other fieldsHigh correlation
CAN_AL_parsed is highly correlated with CAN_AL and 17 other fieldsHigh correlation
CAN_COM_parsed is highly correlated with CAN_AL and 18 other fieldsHigh correlation
CAN_DIF_parsed is highly correlated with EDA and 2 other fieldsHigh correlation
CAN_HOS_parsed is highly correlated with CAN_HOS and 18 other fieldsHigh correlation
CAN_STRA_parsed is highly correlated with Unnamed: 0 and 27 other fieldsHigh correlation
CAN_SEGPOP_parsed is highly correlated with Unnamed: 0 and 27 other fieldsHigh correlation
CAN_OFAM_parsed is highly correlated with Unnamed: 0 and 27 other fieldsHigh correlation
AGUA_AL_parsed is highly correlated with CAN_AL and 7 other fieldsHigh correlation
DESC_BUS_parsed is highly correlated with DESC_BUS and 10 other fieldsHigh correlation
APO_AUX_parsed is highly correlated with Unnamed: 0 and 33 other fieldsHigh correlation
APO_LLAM_parsed is highly correlated with DEL and 21 other fieldsHigh correlation
APO_MAT_parsed is highly correlated with DEL and 22 other fieldsHigh correlation
APO_TRAS_parsed is highly correlated with CAN_STRA and 22 other fieldsHigh correlation
APO_VES_parsed is highly correlated with Unnamed: 0 and 31 other fieldsHigh correlation
APO_ACT_NAC_parsed is highly correlated with Unnamed: 0 and 29 other fieldsHigh correlation
APO_CURP_parsed is highly correlated with CAN_HOS and 30 other fieldsHigh correlation
APO_ASF_parsed is highly correlated with Unnamed: 0 and 25 other fieldsHigh correlation
APO_AME_parsed is highly correlated with Unnamed: 0 and 27 other fieldsHigh correlation
APO_REC_PERT_parsed is highly correlated with Unnamed: 0 and 35 other fieldsHigh correlation
APO_AT_MEDICA_parsed is highly correlated with DEL and 23 other fieldsHigh correlation
APO_TRASF has 234056 (77.7%) missing values Missing
APO_CURP has 67167 (22.3%) missing values Missing
APO_ASF has 67167 (22.3%) missing values Missing
APO_AME has 67167 (22.3%) missing values Missing
APO_REC_PERT has 67167 (22.3%) missing values Missing
APO_AT_MEDICA has 67167 (22.3%) missing values Missing
MUN_NAC has 193153 (64.1%) missing values Missing
FEC_REP_parsed has 301223 (100.0%) missing values Missing
IRE_parsed has 301223 (100.0%) missing values Missing
EDA_parsed has 301223 (100.0%) missing values Missing
APO_TRASF_parsed has 301223 (100.0%) missing values Missing
APO_CURP_parsed has 67167 (22.3%) missing values Missing
APO_ASF_parsed has 67167 (22.3%) missing values Missing
APO_AME_parsed has 67167 (22.3%) missing values Missing
APO_REC_PERT_parsed has 67167 (22.3%) missing values Missing
APO_AT_MEDICA_parsed has 67167 (22.3%) missing values Missing
MUN_NAC_parsed has 193153 (64.1%) missing values Missing
NIV_ESC is highly skewed (γ1 = 59.49651844) Skewed
ACOM_REP is highly skewed (γ1 = 43.76766009) Skewed
EDO_DET is highly skewed (γ1 = 43.21096469) Skewed
EN_DES is highly skewed (γ1 = 66.69611606) Skewed
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
IRE is an unsupported type, check if it needs cleaning or further analysis Unsupported
FEC_REP_parsed is an unsupported type, check if it needs cleaning or further analysis Unsupported
IRE_parsed is an unsupported type, check if it needs cleaning or further analysis Unsupported
EDA_parsed is an unsupported type, check if it needs cleaning or further analysis Unsupported
APO_TRASF_parsed is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-10-31 22:36:03.503050
Analysis finished2022-10-31 22:39:50.297957
Duration3 minutes and 46.79 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct301223
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150611
Minimum0
Maximum301222
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-10-31T15:39:50.467957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15061.1
Q175305.5
median150611
Q3225916.5
95-th percentile286160.9
Maximum301222
Range301222
Interquartile range (IQR)150611

Descriptive statistics

Standard deviation86955.73441
Coefficient of variation (CV)0.5773531442
Kurtosis-1.2
Mean150611
Median Absolute Deviation (MAD)75306
Skewness0
Sum4.536749725 Ɨ 1010
Variance7561299746
MonotonicityStrictly increasing
2022-10-31T15:39:50.921959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
2008031
 
< 0.1%
2008191
 
< 0.1%
2008181
 
< 0.1%
2008171
 
< 0.1%
2008161
 
< 0.1%
2008151
 
< 0.1%
2008141
 
< 0.1%
2008131
 
< 0.1%
2008121
 
< 0.1%
Other values (301213)301213
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
3012221
< 0.1%
3012211
< 0.1%
3012201
< 0.1%
3012191
< 0.1%
3012181
< 0.1%
3012171
< 0.1%
3012161
< 0.1%
3012151
< 0.1%
3012141
< 0.1%
3012131
< 0.1%

DEL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.04085677
Minimum2
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-10-31T15:39:51.096958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q126
median26
Q326
95-th percentile26
Maximum28
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.165717556
Coefficient of variation (CV)0.4356152249
Kurtosis-0.1103497133
Mean21.04085677
Median Absolute Deviation (MAD)0
Skewness-1.320424652
Sum6337990
Variance84.01037831
MonotonicityNot monotonic
2022-10-31T15:39:51.256957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
26213618
70.9%
236288
 
12.0%
721788
 
7.2%
2812958
 
4.3%
146278
 
2.1%
66257
 
2.1%
91425
 
0.5%
21771
 
0.3%
27769
 
0.3%
22609
 
0.2%
Other values (2)462
 
0.2%
ValueCountFrequency (%)
236288
 
12.0%
66257
 
2.1%
721788
 
7.2%
91425
 
0.5%
146278
 
2.1%
16461
 
0.2%
191
 
< 0.1%
21771
 
0.3%
22609
 
0.2%
26213618
70.9%
ValueCountFrequency (%)
2812958
 
4.3%
27769
 
0.3%
26213618
70.9%
22609
 
0.2%
21771
 
0.3%
191
 
< 0.1%
16461
 
0.2%
146278
 
2.1%
91425
 
0.5%
721788
 
7.2%

FEC_REP
Categorical

HIGH CARDINALITY

Distinct2192
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size19.2 MiB
2020-02-28
 
527
2020-02-07
 
394
2020-01-24
 
386
2020-03-17
 
383
2020-03-06
 
377
Other values (2187)
299156 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3012230
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016-06-06
2nd row2016-01-22
3rd row2016-11-10
4th row2016-07-10
5th row2016-06-03

Common Values

ValueCountFrequency (%)
2020-02-28527
 
0.2%
2020-02-07394
 
0.1%
2020-01-24386
 
0.1%
2020-03-17383
 
0.1%
2020-03-06377
 
0.1%
2016-03-24370
 
0.1%
2020-02-03367
 
0.1%
2016-03-23364
 
0.1%
2020-03-19363
 
0.1%
2016-05-14349
 
0.1%
Other values (2182)297343
98.7%

Length

2022-10-31T15:39:51.422074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-02-28527
 
0.2%
2020-02-07394
 
0.1%
2020-01-24386
 
0.1%
2020-03-17383
 
0.1%
2020-03-06377
 
0.1%
2016-03-24370
 
0.1%
2020-02-03367
 
0.1%
2016-03-23364
 
0.1%
2020-03-19363
 
0.1%
2016-05-14349
 
0.1%
Other values (2182)297343
98.7%

Most occurring characters

ValueCountFrequency (%)
0726709
24.1%
-602446
20.0%
2586284
19.5%
1507926
16.9%
6121238
 
4.0%
896980
 
3.2%
792384
 
3.1%
991491
 
3.0%
375659
 
2.5%
456904
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2409784
80.0%
Dash Punctuation602446
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0726709
30.2%
2586284
24.3%
1507926
21.1%
6121238
 
5.0%
896980
 
4.0%
792384
 
3.8%
991491
 
3.8%
375659
 
3.1%
456904
 
2.4%
554209
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-602446
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3012230
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0726709
24.1%
-602446
20.0%
2586284
19.5%
1507926
16.9%
6121238
 
4.0%
896980
 
3.2%
792384
 
3.1%
991491
 
3.0%
375659
 
2.5%
456904
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3012230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0726709
24.1%
-602446
20.0%
2586284
19.5%
1507926
16.9%
6121238
 
4.0%
896980
 
3.2%
792384
 
3.1%
991491
 
3.0%
375659
 
2.5%
456904
 
1.9%

CLASIF_REP
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
1
155411 
3
89506 
2
56306 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1155411
51.6%
389506
29.7%
256306
 
18.7%

Length

2022-10-31T15:39:51.590156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:39:51.789125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1155411
51.6%
389506
29.7%
256306
 
18.7%

Most occurring characters

ValueCountFrequency (%)
1155411
51.6%
389506
29.7%
256306
 
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1155411
51.6%
389506
29.7%
256306
 
18.7%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1155411
51.6%
389506
29.7%
256306
 
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1155411
51.6%
389506
29.7%
256306
 
18.7%

IRE
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size15.3 MiB

SEXO
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
1
272739 
2
28484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1272739
90.5%
228484
 
9.5%

Length

2022-10-31T15:39:51.945158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:39:52.121189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1272739
90.5%
228484
 
9.5%

Most occurring characters

ValueCountFrequency (%)
1272739
90.5%
228484
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1272739
90.5%
228484
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1272739
90.5%
228484
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1272739
90.5%
228484
 
9.5%

EDA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct89
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.99327077
Minimum0
Maximum100
Zeros76
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-10-31T15:39:52.304318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q122
median29
Q336
95-th percentile48
Maximum100
Range100
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.952868688
Coefficient of variation (CV)0.3318367232
Kurtosis0.2056697646
Mean29.99327077
Median Absolute Deviation (MAD)7
Skewness0.6130503787
Sum9034663
Variance99.05959512
MonotonicityNot monotonic
2022-10-31T15:39:52.519319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2012785
 
4.2%
2112648
 
4.2%
2412638
 
4.2%
2212444
 
4.1%
2312369
 
4.1%
2512068
 
4.0%
2611814
 
3.9%
1911636
 
3.9%
2711334
 
3.8%
2811026
 
3.7%
Other values (79)180461
59.9%
ValueCountFrequency (%)
076
< 0.1%
1145
< 0.1%
2144
< 0.1%
3151
0.1%
4169
0.1%
5164
0.1%
6138
< 0.1%
7150
< 0.1%
8150
< 0.1%
9157
0.1%
ValueCountFrequency (%)
1002
 
< 0.1%
991
 
< 0.1%
931
 
< 0.1%
891
 
< 0.1%
881
 
< 0.1%
861
 
< 0.1%
833
< 0.1%
821
 
< 0.1%
813
< 0.1%
795
< 0.1%

EN_NAC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.43041534
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-10-31T15:39:52.709319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q112
median17
Q325
95-th percentile30
Maximum32
Range31
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.360813663
Coefficient of variation (CV)0.4222970893
Kurtosis-0.9412402262
Mean17.43041534
Median Absolute Deviation (MAD)5
Skewness-0.02888270022
Sum5250442
Variance54.18157778
MonotonicityNot monotonic
2022-10-31T15:39:52.900320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1231935
10.6%
2031935
10.6%
2630064
10.0%
728347
9.4%
2526304
 
8.7%
2123266
 
7.7%
1620483
 
6.8%
3015005
 
5.0%
1513346
 
4.4%
1410746
 
3.6%
Other values (22)69792
23.2%
ValueCountFrequency (%)
11187
 
0.4%
25566
 
1.8%
3291
 
0.1%
4692
 
0.2%
5989
 
0.3%
61311
 
0.4%
728347
9.4%
86056
 
2.0%
95540
 
1.8%
104355
 
1.4%
ValueCountFrequency (%)
323106
 
1.0%
31694
 
0.2%
3015005
5.0%
291474
 
0.5%
282113
 
0.7%
271374
 
0.5%
2630064
10.0%
2526304
8.7%
242530
 
0.8%
23914
 
0.3%

NIV_ESC
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.249625693
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-10-31T15:39:53.066320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q35
95-th percentile7
Maximum9999
Range9998
Interquartile range (IQR)2

Descriptive statistics

Standard deviation167.8763077
Coefficient of variation (CV)23.15654833
Kurtosis3538.19263
Mean7.249625693
Median Absolute Deviation (MAD)1
Skewness59.49651844
Sum2183754
Variance28182.4547
MonotonicityNot monotonic
2022-10-31T15:39:53.239447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5124098
41.2%
363543
21.1%
732136
 
10.7%
427615
 
9.2%
223437
 
7.8%
612898
 
4.3%
111407
 
3.8%
93186
 
1.1%
82668
 
0.9%
11121
 
< 0.1%
Other values (2)114
 
< 0.1%
ValueCountFrequency (%)
111407
 
3.8%
223437
 
7.8%
363543
21.1%
427615
 
9.2%
5124098
41.2%
612898
 
4.3%
732136
 
10.7%
82668
 
0.9%
93186
 
1.1%
1029
 
< 0.1%
ValueCountFrequency (%)
999985
 
< 0.1%
11121
 
< 0.1%
1029
 
< 0.1%
93186
 
1.1%
82668
 
0.9%
732136
 
10.7%
612898
 
4.3%
5124098
41.2%
427615
 
9.2%
363543
21.1%

ACOM_REP
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.154118377
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-10-31T15:39:53.395447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q33
95-th percentile3
Maximum9999
Range9998
Interquartile range (IQR)0

Descriptive statistics

Standard deviation228.1513584
Coefficient of variation (CV)27.97989283
Kurtosis1913.630575
Mean8.154118377
Median Absolute Deviation (MAD)0
Skewness43.76766009
Sum2456208
Variance52053.04232
MonotonicityNot monotonic
2022-10-31T15:39:53.556448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3270891
89.9%
113199
 
4.4%
47671
 
2.5%
25572
 
1.8%
53733
 
1.2%
9999157
 
0.1%
ValueCountFrequency (%)
113199
 
4.4%
25572
 
1.8%
3270891
89.9%
47671
 
2.5%
53733
 
1.2%
9999157
 
0.1%
ValueCountFrequency (%)
9999157
 
0.1%
53733
 
1.2%
47671
 
2.5%
3270891
89.9%
25572
 
1.8%
113199
 
4.4%

PERM_EU
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.44160306
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-10-31T15:39:53.714447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum9999
Range9998
Interquartile range (IQR)1

Descriptive statistics

Standard deviation638.3205359
Coefficient of variation (CV)15.03997233
Kurtosis239.3074297
Mean42.44160306
Median Absolute Deviation (MAD)0
Skewness15.53399656
Sum12784387
Variance407453.1066
MonotonicityNot monotonic
2022-10-31T15:39:53.873447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1217204
72.1%
255786
 
18.5%
47186
 
2.4%
36734
 
2.2%
65678
 
1.9%
54172
 
1.4%
72870
 
1.0%
99991233
 
0.4%
8360
 
0.1%
ValueCountFrequency (%)
1217204
72.1%
255786
 
18.5%
36734
 
2.2%
47186
 
2.4%
54172
 
1.4%
65678
 
1.9%
72870
 
1.0%
8360
 
0.1%
99991233
 
0.4%
ValueCountFrequency (%)
99991233
 
0.4%
8360
 
0.1%
72870
 
1.0%
65678
 
1.9%
54172
 
1.4%
47186
 
2.4%
36734
 
2.2%
255786
 
18.5%
1217204
72.1%

EDO_DET
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.65532844
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-10-31T15:39:54.060447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median4
Q34
95-th percentile48
Maximum9999
Range9998
Interquartile range (IQR)0

Descriptive statistics

Standard deviation230.4736349
Coefficient of variation (CV)18.21158857
Kurtosis1869.333239
Mean12.65532844
Median Absolute Deviation (MAD)0
Skewness43.21096469
Sum3812076
Variance53118.09638
MonotonicityNot monotonic
2022-10-31T15:39:54.276448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4229851
76.3%
647094
 
15.6%
4817833
 
5.9%
35878
 
0.3%
5618
 
0.2%
13450
 
0.1%
12389
 
0.1%
2362
 
0.1%
32331
 
0.1%
37295
 
0.1%
Other values (41)3122
 
1.0%
ValueCountFrequency (%)
1257
 
0.1%
2362
 
0.1%
4229851
76.3%
5618
 
0.2%
647094
 
15.6%
8217
 
0.1%
93
 
< 0.1%
109
 
< 0.1%
111
 
< 0.1%
12389
 
0.1%
ValueCountFrequency (%)
9999160
 
0.1%
607
 
< 0.1%
567
 
< 0.1%
5555
 
< 0.1%
543
 
< 0.1%
53173
 
0.1%
5159
 
< 0.1%
505
 
< 0.1%
49124
 
< 0.1%
4817833
5.9%

AUT_DEP
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
1
148438 
3
74279 
2
63148 
9999
15282 
4
 
76

Length

Max length4
Median length1
Mean length1.152199533
Min length1

Characters and Unicode

Total characters347069
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1148438
49.3%
374279
24.7%
263148
21.0%
999915282
 
5.1%
476
 
< 0.1%

Length

2022-10-31T15:39:54.621449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:39:54.800447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1148438
49.3%
374279
24.7%
263148
21.0%
999915282
 
5.1%
476
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1148438
42.8%
374279
21.4%
263148
18.2%
961128
17.6%
476
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number347069
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1148438
42.8%
374279
21.4%
263148
18.2%
961128
17.6%
476
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common347069
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1148438
42.8%
374279
21.4%
263148
18.2%
961128
17.6%
476
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII347069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1148438
42.8%
374279
21.4%
263148
18.2%
961128
17.6%
476
 
< 0.1%

EN_DES
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.45959638
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-10-31T15:39:54.968446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median16
Q325
95-th percentile30
Maximum9999
Range9998
Interquartile range (IQR)16

Descriptive statistics

Standard deviation149.1135624
Coefficient of variation (CV)8.077834387
Kurtosis4461.207886
Mean18.45959638
Median Absolute Deviation (MAD)8
Skewness66.69611606
Sum5560455
Variance22234.85449
MonotonicityNot monotonic
2022-10-31T15:39:55.147447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2641610
13.8%
235969
11.9%
1225818
 
8.6%
2025068
 
8.3%
724238
 
8.0%
2522288
 
7.4%
2120178
 
6.7%
1615191
 
5.0%
1512068
 
4.0%
3012016
 
4.0%
Other values (23)66779
22.2%
ValueCountFrequency (%)
11075
 
0.4%
235969
11.9%
3383
 
0.1%
4598
 
0.2%
51211
 
0.4%
61007
 
0.3%
724238
8.0%
85983
 
2.0%
95689
 
1.9%
103508
 
1.2%
ValueCountFrequency (%)
999967
 
< 0.1%
322532
 
0.8%
31541
 
0.2%
3012016
 
4.0%
291223
 
0.4%
282752
 
0.9%
271006
 
0.3%
2641610
13.8%
2522288
7.4%
242256
 
0.7%

MUN_DES
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2288
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14916.53958
Minimum1001
Maximum32058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-10-31T15:39:55.345519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2004
Q19999
median12043
Q321115
95-th percentile28003
Maximum32058
Range31057
Interquartile range (IQR)11116

Descriptive statistics

Standard deviation7893.975369
Coefficient of variation (CV)0.5292095613
Kurtosis-0.9458478637
Mean14916.53958
Median Absolute Deviation (MAD)4943
Skewness0.3254765656
Sum4493204803
Variance62314847.12
MonotonicityNot monotonic
2022-10-31T15:39:55.546521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999980219
26.6%
200414449
 
4.8%
200677581
 
2.5%
20027162
 
2.4%
260436700
 
2.2%
250065191
 
1.7%
260554965
 
1.6%
260023571
 
1.2%
260303404
 
1.1%
260172644
 
0.9%
Other values (2278)165337
54.9%
ValueCountFrequency (%)
1001458
0.2%
100247
 
< 0.1%
100379
 
< 0.1%
10049
 
< 0.1%
100543
 
< 0.1%
100631
 
< 0.1%
100748
 
< 0.1%
100822
 
< 0.1%
100914
 
< 0.1%
101018
 
< 0.1%
ValueCountFrequency (%)
320584
 
< 0.1%
3205719
 
< 0.1%
32056470
0.2%
3205529
 
< 0.1%
3205425
 
< 0.1%
3205314
 
< 0.1%
3205210
 
< 0.1%
3205172
 
< 0.1%
320502
 
< 0.1%
3204933
 
< 0.1%

CAN_AL
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
0
158193 
1
143030 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0158193
52.5%
1143030
47.5%

Length

2022-10-31T15:39:55.725520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:39:55.891520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0158193
52.5%
1143030
47.5%

Most occurring characters

ValueCountFrequency (%)
0158193
52.5%
1143030
47.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0158193
52.5%
1143030
47.5%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0158193
52.5%
1143030
47.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0158193
52.5%
1143030
47.5%

CAN_COM
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
0
173520 
1
127703 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0173520
57.6%
1127703
42.4%

Length

2022-10-31T15:39:56.047520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:39:56.226598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0173520
57.6%
1127703
42.4%

Most occurring characters

ValueCountFrequency (%)
0173520
57.6%
1127703
42.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0173520
57.6%
1127703
42.4%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0173520
57.6%
1127703
42.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0173520
57.6%
1127703
42.4%

CAN_DIF
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
0
286079 
1
 
15144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0286079
95.0%
115144
 
5.0%

Length

2022-10-31T15:39:56.377618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:39:56.546618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0286079
95.0%
115144
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0286079
95.0%
115144
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0286079
95.0%
115144
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0286079
95.0%
115144
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0286079
95.0%
115144
 
5.0%

CAN_HOS
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
0
293038 
1
 
8185

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0293038
97.3%
18185
 
2.7%

Length

2022-10-31T15:39:56.694618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:39:56.859618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0293038
97.3%
18185
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0293038
97.3%
18185
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0293038
97.3%
18185
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0293038
97.3%
18185
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0293038
97.3%
18185
 
2.7%

CAN_STRA
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
0
272940 
1
28283 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0272940
90.6%
128283
 
9.4%

Length

2022-10-31T15:39:57.006640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:39:57.169639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0272940
90.6%
128283
 
9.4%

Most occurring characters

ValueCountFrequency (%)
0272940
90.6%
128283
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0272940
90.6%
128283
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0272940
90.6%
128283
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0272940
90.6%
128283
 
9.4%

CAN_SEGPOP
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
0
249626 
1
51597 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0249626
82.9%
151597
 
17.1%

Length

2022-10-31T15:39:57.315639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:39:57.486219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0249626
82.9%
151597
 
17.1%

Most occurring characters

ValueCountFrequency (%)
0249626
82.9%
151597
 
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0249626
82.9%
151597
 
17.1%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0249626
82.9%
151597
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0249626
82.9%
151597
 
17.1%

CAN_OFAM
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
0
275524 
1
 
25699

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0275524
91.5%
125699
 
8.5%

Length

2022-10-31T15:39:57.635219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:39:57.803221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0275524
91.5%
125699
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0275524
91.5%
125699
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0275524
91.5%
125699
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0275524
91.5%
125699
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0275524
91.5%
125699
 
8.5%

AGUA_AL
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
1
250502 
0
50721 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1250502
83.2%
050721
 
16.8%

Length

2022-10-31T15:39:57.960179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:39:58.130177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1250502
83.2%
050721
 
16.8%

Most occurring characters

ValueCountFrequency (%)
1250502
83.2%
050721
 
16.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1250502
83.2%
050721
 
16.8%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1250502
83.2%
050721
 
16.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1250502
83.2%
050721
 
16.8%

DESC_BUS
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
0
153377 
1
147846 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0153377
50.9%
1147846
49.1%

Length

2022-10-31T15:39:58.289174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:39:58.626177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0153377
50.9%
1147846
49.1%

Most occurring characters

ValueCountFrequency (%)
0153377
50.9%
1147846
49.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0153377
50.9%
1147846
49.1%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0153377
50.9%
1147846
49.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0153377
50.9%
1147846
49.1%

APO_AUX
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
0
269197 
1
32026 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0269197
89.4%
132026
 
10.6%

Length

2022-10-31T15:39:58.778986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:39:58.936988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0269197
89.4%
132026
 
10.6%

Most occurring characters

ValueCountFrequency (%)
0269197
89.4%
132026
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0269197
89.4%
132026
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0269197
89.4%
132026
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0269197
89.4%
132026
 
10.6%

APO_LLAM
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
0
156627 
1
144596 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0156627
52.0%
1144596
48.0%

Length

2022-10-31T15:39:59.083983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:39:59.242991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0156627
52.0%
1144596
48.0%

Most occurring characters

ValueCountFrequency (%)
0156627
52.0%
1144596
48.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0156627
52.0%
1144596
48.0%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0156627
52.0%
1144596
48.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0156627
52.0%
1144596
48.0%

APO_MAT
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
0
197769 
1
103454 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0197769
65.7%
1103454
34.3%

Length

2022-10-31T15:39:59.388171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:39:59.555088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0197769
65.7%
1103454
34.3%

Most occurring characters

ValueCountFrequency (%)
0197769
65.7%
1103454
34.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0197769
65.7%
1103454
34.3%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0197769
65.7%
1103454
34.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0197769
65.7%
1103454
34.3%

APO_TRAS
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
0
198588 
1
102635 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0198588
65.9%
1102635
34.1%

Length

2022-10-31T15:39:59.710178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:39:59.887268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0198588
65.9%
1102635
34.1%

Most occurring characters

ValueCountFrequency (%)
0198588
65.9%
1102635
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0198588
65.9%
1102635
34.1%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0198588
65.9%
1102635
34.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0198588
65.9%
1102635
34.1%

APO_VES
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
0
264478 
1
36745 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0264478
87.8%
136745
 
12.2%

Length

2022-10-31T15:40:00.047268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:00.216274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0264478
87.8%
136745
 
12.2%

Most occurring characters

ValueCountFrequency (%)
0264478
87.8%
136745
 
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0264478
87.8%
136745
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0264478
87.8%
136745
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0264478
87.8%
136745
 
12.2%

APO_ACT_NAC
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
0
273978 
1
 
27245

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301223
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0273978
91.0%
127245
 
9.0%

Length

2022-10-31T15:40:00.369268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:00.534268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0273978
91.0%
127245
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0273978
91.0%
127245
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number301223
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0273978
91.0%
127245
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common301223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0273978
91.0%
127245
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII301223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0273978
91.0%
127245
 
9.0%

APO_TRASF
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing234056
Missing (%)77.7%
Memory size12.8 MiB
0.0
67167 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters201501
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.067167
 
22.3%
(Missing)234056
77.7%

Length

2022-10-31T15:40:00.686268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:00.848288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.067167
100.0%

Most occurring characters

ValueCountFrequency (%)
0134334
66.7%
.67167
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number134334
66.7%
Other Punctuation67167
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0134334
100.0%
Other Punctuation
ValueCountFrequency (%)
.67167
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common201501
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0134334
66.7%
.67167
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII201501
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0134334
66.7%
.67167
33.3%

APO_CURP
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing67167
Missing (%)22.3%
Memory size16.0 MiB
0.0
198501 
1.0
35555 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702168
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0198501
65.9%
1.035555
 
11.8%
(Missing)67167
 
22.3%

Length

2022-10-31T15:40:00.988287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:01.141287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0198501
84.8%
1.035555
 
15.2%

Most occurring characters

ValueCountFrequency (%)
0432557
61.6%
.234056
33.3%
135555
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number468112
66.7%
Other Punctuation234056
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0432557
92.4%
135555
 
7.6%
Other Punctuation
ValueCountFrequency (%)
.234056
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common702168
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0432557
61.6%
.234056
33.3%
135555
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII702168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0432557
61.6%
.234056
33.3%
135555
 
5.1%

APO_ASF
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing67167
Missing (%)22.3%
Memory size16.0 MiB
0.0
155973 
1.0
78083 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702168
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0155973
51.8%
1.078083
25.9%
(Missing)67167
22.3%

Length

2022-10-31T15:40:01.283288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:01.443288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0155973
66.6%
1.078083
33.4%

Most occurring characters

ValueCountFrequency (%)
0390029
55.5%
.234056
33.3%
178083
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number468112
66.7%
Other Punctuation234056
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0390029
83.3%
178083
 
16.7%
Other Punctuation
ValueCountFrequency (%)
.234056
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common702168
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0390029
55.5%
.234056
33.3%
178083
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII702168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0390029
55.5%
.234056
33.3%
178083
 
11.1%

APO_AME
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing67167
Missing (%)22.3%
Memory size16.0 MiB
0.0
170236 
1.0
63820 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702168
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0170236
56.5%
1.063820
 
21.2%
(Missing)67167
 
22.3%

Length

2022-10-31T15:40:01.591288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:01.757077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0170236
72.7%
1.063820
 
27.3%

Most occurring characters

ValueCountFrequency (%)
0404292
57.6%
.234056
33.3%
163820
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number468112
66.7%
Other Punctuation234056
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0404292
86.4%
163820
 
13.6%
Other Punctuation
ValueCountFrequency (%)
.234056
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common702168
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0404292
57.6%
.234056
33.3%
163820
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII702168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0404292
57.6%
.234056
33.3%
163820
 
9.1%

APO_REC_PERT
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing67167
Missing (%)22.3%
Memory size16.0 MiB
0.0
207231 
1.0
26825 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702168
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0207231
68.8%
1.026825
 
8.9%
(Missing)67167
 
22.3%

Length

2022-10-31T15:40:01.904102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:02.062105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0207231
88.5%
1.026825
 
11.5%

Most occurring characters

ValueCountFrequency (%)
0441287
62.8%
.234056
33.3%
126825
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number468112
66.7%
Other Punctuation234056
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0441287
94.3%
126825
 
5.7%
Other Punctuation
ValueCountFrequency (%)
.234056
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common702168
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0441287
62.8%
.234056
33.3%
126825
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII702168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0441287
62.8%
.234056
33.3%
126825
 
3.8%

APO_AT_MEDICA
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing67167
Missing (%)22.3%
Memory size16.0 MiB
0.0
186069 
1.0
47987 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters702168
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0186069
61.8%
1.047987
 
15.9%
(Missing)67167
 
22.3%

Length

2022-10-31T15:40:02.208102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:02.370753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0186069
79.5%
1.047987
 
20.5%

Most occurring characters

ValueCountFrequency (%)
0420125
59.8%
.234056
33.3%
147987
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number468112
66.7%
Other Punctuation234056
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0420125
89.7%
147987
 
10.3%
Other Punctuation
ValueCountFrequency (%)
.234056
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common702168
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0420125
59.8%
.234056
33.3%
147987
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII702168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0420125
59.8%
.234056
33.3%
147987
 
6.8%

MUN_NAC
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct2164
Distinct (%)2.0%
Missing193153
Missing (%)64.1%
Infinite0
Infinite (%)0.0%
Mean14801.34122
Minimum1001
Maximum32058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-10-31T15:40:02.534752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile7046
Q19999
median11026
Q320450
95-th percentile28041
Maximum32058
Range31057
Interquartile range (IQR)10451

Descriptive statistics

Standard deviation7037.68688
Coefficient of variation (CV)0.4754762946
Kurtosis-0.5276039539
Mean14801.34122
Median Absolute Deviation (MAD)3007
Skewness0.782564008
Sum1599580946
Variance49529036.62
MonotonicityNot monotonic
2022-10-31T15:40:02.907290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999938786
 
12.9%
200671590
 
0.5%
250061348
 
0.4%
260431191
 
0.4%
21114955
 
0.3%
12066753
 
0.2%
25011687
 
0.2%
26055685
 
0.2%
12001594
 
0.2%
16053590
 
0.2%
Other values (2154)60891
 
20.2%
(Missing)193153
64.1%
ValueCountFrequency (%)
1001172
0.1%
100222
 
< 0.1%
100332
 
< 0.1%
100514
 
< 0.1%
100614
 
< 0.1%
100730
 
< 0.1%
10088
 
< 0.1%
10098
 
< 0.1%
10108
 
< 0.1%
101115
 
< 0.1%
ValueCountFrequency (%)
320581
 
< 0.1%
3205711
 
< 0.1%
32056130
< 0.1%
320554
 
< 0.1%
3205416
 
< 0.1%
3205312
 
< 0.1%
3205213
 
< 0.1%
3205131
 
< 0.1%
3204916
 
< 0.1%
3204816
 
< 0.1%

DEL_parsed
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size18.6 MiB
Sonora
213618 
Baja California
36288 
Coahuila
21788 
Tamaulipas
 
12958
Jalisco
 
6278
Other values (6)
 
10292

Length

Max length16
Median length6
Mean length7.544634854
Min length6

Characters and Unicode

Total characters2272610
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSonora
2nd rowSonora
3rd rowSonora
4th rowSonora
5th rowSonora

Common Values

ValueCountFrequency (%)
Sonora213618
70.9%
Baja California36288
 
12.0%
Coahuila21788
 
7.2%
Tamaulipas12958
 
4.3%
Jalisco6278
 
2.1%
Chihuahua6257
 
2.1%
Ciudad de MƩxico1425
 
0.5%
Puebla771
 
0.3%
Tabasco769
 
0.3%
QuerƩtaro609
 
0.2%

Length

2022-10-31T15:40:03.083288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sonora213618
62.8%
baja36288
 
10.7%
california36288
 
10.7%
coahuila21788
 
6.4%
tamaulipas12958
 
3.8%
jalisco6278
 
1.8%
chihuahua6257
 
1.8%
ciudad1425
 
0.4%
de1425
 
0.4%
mƩxico1425
 
0.4%
Other values (4)2610
 
0.8%

Most occurring characters

ValueCountFrequency (%)
o494854
21.8%
a464816
20.5%
r251124
11.1%
n250367
11.0%
S213618
9.4%
i123168
 
5.4%
l78083
 
3.4%
C65758
 
2.9%
u50065
 
2.2%
h41020
 
1.8%
Other values (20)239737
10.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1894537
83.4%
Uppercase Letter338935
 
14.9%
Space Separator39138
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o494854
26.1%
a464816
24.5%
r251124
13.3%
n250367
13.2%
i123168
 
6.5%
l78083
 
4.1%
u50065
 
2.6%
h41020
 
2.2%
j36288
 
1.9%
f36288
 
1.9%
Other values (11)68464
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
S213618
63.0%
C65758
 
19.4%
B36288
 
10.7%
T13727
 
4.1%
J6278
 
1.9%
M1886
 
0.6%
P771
 
0.2%
Q609
 
0.2%
Space Separator
ValueCountFrequency (%)
39138
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2233472
98.3%
Common39138
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o494854
22.2%
a464816
20.8%
r251124
11.2%
n250367
11.2%
S213618
9.6%
i123168
 
5.5%
l78083
 
3.5%
C65758
 
2.9%
u50065
 
2.2%
h41020
 
1.8%
Other values (19)200599
9.0%
Common
ValueCountFrequency (%)
39138
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2270115
99.9%
None2495
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o494854
21.8%
a464816
20.5%
r251124
11.1%
n250367
11.0%
S213618
9.4%
i123168
 
5.4%
l78083
 
3.4%
C65758
 
2.9%
u50065
 
2.2%
h41020
 
1.8%
Other values (18)237242
10.5%
None
ValueCountFrequency (%)
Ʃ2034
81.5%
Ɣ461
 
18.5%

FEC_REP_parsed
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing301223
Missing (%)100.0%
Memory size2.3 MiB

CLASIF_REP_parsed
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.7 MiB
Repatriado de no reincidencia
155411 
Reincidencias posteriores a la primera
89506 
Repatriado de primera reincidencia
56306 

Length

Max length38
Median length29
Mean length32.60890105
Min length29

Characters and Unicode

Total characters9822551
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRepatriado de no reincidencia
2nd rowRepatriado de no reincidencia
3rd rowRepatriado de no reincidencia
4th rowRepatriado de no reincidencia
5th rowRepatriado de no reincidencia

Common Values

ValueCountFrequency (%)
Repatriado de no reincidencia155411
51.6%
Reincidencias posteriores a la primera89506
29.7%
Repatriado de primera reincidencia56306
 
18.7%

Length

2022-10-31T15:40:03.260872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:03.432873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
repatriado211717
16.4%
de211717
16.4%
reincidencia211717
16.4%
no155411
12.0%
primera145812
11.3%
reincidencias89506
6.9%
posteriores89506
6.9%
a89506
6.9%
la89506
6.9%

Most occurring characters

ValueCountFrequency (%)
e1350704
13.8%
i1350704
13.8%
a1049481
10.7%
993175
10.1%
r894070
9.1%
n757857
7.7%
d724657
7.4%
c602446
6.1%
o546140
5.6%
p447035
 
4.6%
Other values (5)1106282
11.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8528153
86.8%
Space Separator993175
 
10.1%
Uppercase Letter301223
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1350704
15.8%
i1350704
15.8%
a1049481
12.3%
r894070
10.5%
n757857
8.9%
d724657
8.5%
c602446
7.1%
o546140
6.4%
p447035
 
5.2%
t301223
 
3.5%
Other values (3)503836
 
5.9%
Space Separator
ValueCountFrequency (%)
993175
100.0%
Uppercase Letter
ValueCountFrequency (%)
R301223
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8829376
89.9%
Common993175
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1350704
15.3%
i1350704
15.3%
a1049481
11.9%
r894070
10.1%
n757857
8.6%
d724657
8.2%
c602446
6.8%
o546140
6.2%
p447035
 
5.1%
R301223
 
3.4%
Other values (4)805059
9.1%
Common
ValueCountFrequency (%)
993175
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9822551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1350704
13.8%
i1350704
13.8%
a1049481
10.7%
993175
10.1%
r894070
9.1%
n757857
7.7%
d724657
7.4%
c602446
6.1%
o546140
5.6%
p447035
 
4.6%
Other values (5)1106282
11.3%

IRE_parsed
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing301223
Missing (%)100.0%
Memory size2.3 MiB

SEXO_parsed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.1 MiB
Hombre
272739 
Mujer
28484 

Length

Max length6
Median length6
Mean length5.905438828
Min length5

Characters and Unicode

Total characters1778854
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHombre
2nd rowHombre
3rd rowHombre
4th rowHombre
5th rowHombre

Common Values

ValueCountFrequency (%)
Hombre272739
90.5%
Mujer28484
 
9.5%

Length

2022-10-31T15:40:03.591873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:03.759897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
hombre272739
90.5%
mujer28484
 
9.5%

Most occurring characters

ValueCountFrequency (%)
r301223
16.9%
e301223
16.9%
H272739
15.3%
o272739
15.3%
m272739
15.3%
b272739
15.3%
M28484
 
1.6%
u28484
 
1.6%
j28484
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1477631
83.1%
Uppercase Letter301223
 
16.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r301223
20.4%
e301223
20.4%
o272739
18.5%
m272739
18.5%
b272739
18.5%
u28484
 
1.9%
j28484
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
H272739
90.5%
M28484
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
Latin1778854
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r301223
16.9%
e301223
16.9%
H272739
15.3%
o272739
15.3%
m272739
15.3%
b272739
15.3%
M28484
 
1.6%
u28484
 
1.6%
j28484
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1778854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r301223
16.9%
e301223
16.9%
H272739
15.3%
o272739
15.3%
m272739
15.3%
b272739
15.3%
M28484
 
1.6%
u28484
 
1.6%
j28484
 
1.6%

EDA_parsed
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing301223
Missing (%)100.0%
Memory size2.3 MiB

EN_NAC_parsed
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.6 MiB
Guerrero
31935 
Oaxaca
31935 
Sonora
30064 
Chiapas
28347 
Sinaloa
26304 
Other values (27)
152638 

Length

Max length19
Median length16
Mean length7.60884129
Min length6

Characters and Unicode

Total characters2291958
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJalisco
2nd rowPuebla
3rd rowGuerrero
4th rowJalisco
5th rowJalisco

Common Values

ValueCountFrequency (%)
Guerrero31935
10.6%
Oaxaca31935
10.6%
Sonora30064
10.0%
Chiapas28347
9.4%
Sinaloa26304
 
8.7%
Puebla23266
 
7.7%
MichoacƔn20483
 
6.8%
Veracruz15005
 
5.0%
MƩxico13346
 
4.4%
Jalisco10746
 
3.6%
Other values (22)69792
23.2%

Length

2022-10-31T15:40:03.924898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
guerrero31935
 
9.8%
oaxaca31935
 
9.8%
sonora30064
 
9.2%
chiapas28347
 
8.7%
sinaloa26304
 
8.1%
puebla23266
 
7.2%
michoacƔn20483
 
6.3%
mƩxico18886
 
5.8%
veracruz15005
 
4.6%
jalisco10746
 
3.3%
Other values (28)88259
27.1%

Most occurring characters

ValueCountFrequency (%)
a434881
19.0%
o220348
 
9.6%
r180782
 
7.9%
i151350
 
6.6%
c129171
 
5.6%
u123636
 
5.4%
e123101
 
5.4%
n103726
 
4.5%
l88443
 
3.9%
h68679
 
3.0%
Other values (37)667841
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1948261
85.0%
Uppercase Letter319690
 
13.9%
Space Separator24007
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a434881
22.3%
o220348
11.3%
r180782
9.3%
i151350
 
7.8%
c129171
 
6.6%
u123636
 
6.3%
e123101
 
6.3%
n103726
 
5.3%
l88443
 
4.5%
h68679
 
3.5%
Other values (17)324144
16.6%
Uppercase Letter
ValueCountFrequency (%)
S59189
18.5%
C48792
15.3%
M43653
13.7%
G41554
13.0%
O31935
10.0%
P25796
8.1%
V15005
 
4.7%
J10746
 
3.4%
H9438
 
3.0%
B5857
 
1.8%
Other values (9)27725
8.7%
Space Separator
ValueCountFrequency (%)
24007
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2267951
99.0%
Common24007
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a434881
19.2%
o220348
 
9.7%
r180782
 
8.0%
i151350
 
6.7%
c129171
 
5.7%
u123636
 
5.5%
e123101
 
5.4%
n103726
 
4.6%
l88443
 
3.9%
h68679
 
3.0%
Other values (36)643834
28.4%
Common
ValueCountFrequency (%)
24007
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2245898
98.0%
None46060
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a434881
19.4%
o220348
 
9.8%
r180782
 
8.0%
i151350
 
6.7%
c129171
 
5.8%
u123636
 
5.5%
e123101
 
5.5%
n103726
 
4.6%
l88443
 
3.9%
h68679
 
3.1%
Other values (33)621781
27.7%
None
ValueCountFrequency (%)
Ʃ21548
46.8%
Ɣ21177
46.0%
Ć­2530
 
5.5%
ó805
 
1.7%

NIV_ESC_parsed
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.5 MiB
Secundaria Completa
124098 
Primaria Completa
63543 
Preparatoria o bachillerato Completo
32136 
Secundaria Incompleta
27615 
Primaria Incompleta
23437 
Other values (7)
30394 

Length

Max length38
Median length36
Mean length21.29183031
Min length15

Characters and Unicode

Total characters6413589
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPreparatoria o bachillerato Completo
2nd rowSecundaria Completa
3rd rowPreparatoria o bachillerato Completo
4th rowPrimaria Incompleta
5th rowPrimaria Incompleta

Common Values

ValueCountFrequency (%)
Secundaria Completa124098
41.2%
Primaria Completa63543
21.1%
Preparatoria o bachillerato Completo32136
 
10.7%
Secundaria Incompleta27615
 
9.2%
Primaria Incompleta23437
 
7.8%
Preparatoria o bachillerato Incompleto12898
 
4.3%
Sin Escolaridad11407
 
3.8%
Licenciatura Completa3186
 
1.1%
Licenciatura Incompleta2668
 
0.9%
Posgrado completo121
 
< 0.1%
Other values (2)114
 
< 0.1%

Length

2022-10-31T15:40:04.099898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
completa190827
27.6%
secundaria151713
21.9%
primaria86980
12.6%
incompleta53720
 
7.8%
preparatoria45034
 
6.5%
o45034
 
6.5%
bachillerato45034
 
6.5%
completo32257
 
4.7%
incompleto12927
 
1.9%
sin11407
 
1.6%
Other values (5)17581
 
2.5%

Most occurring characters

ValueCountFrequency (%)
a981860
15.3%
e537536
 
8.4%
r523220
 
8.2%
o481894
 
7.5%
i450462
 
7.0%
391291
 
6.1%
l391206
 
6.1%
t385653
 
6.0%
m376711
 
5.9%
p334850
 
5.2%
Other values (16)1558906
24.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5420087
84.5%
Uppercase Letter602211
 
9.4%
Space Separator391291
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a981860
18.1%
e537536
9.9%
r523220
9.7%
o481894
8.9%
i450462
8.3%
l391206
 
7.2%
t385653
 
7.1%
m376711
 
7.0%
p334850
 
6.2%
c286800
 
5.3%
Other values (8)669895
12.4%
Uppercase Letter
ValueCountFrequency (%)
C222963
37.0%
S163120
27.1%
P132164
21.9%
I66618
 
11.1%
E11407
 
1.9%
L5854
 
1.0%
N85
 
< 0.1%
Space Separator
ValueCountFrequency (%)
391291
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6022298
93.9%
Common391291
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a981860
16.3%
e537536
8.9%
r523220
8.7%
o481894
 
8.0%
i450462
 
7.5%
l391206
 
6.5%
t385653
 
6.4%
m376711
 
6.3%
p334850
 
5.6%
c286800
 
4.8%
Other values (15)1272106
21.1%
Common
ValueCountFrequency (%)
391291
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6413589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a981860
15.3%
e537536
 
8.4%
r523220
 
8.2%
o481894
 
7.5%
i450462
 
7.0%
391291
 
6.1%
l391206
 
6.1%
t385653
 
6.0%
m376711
 
5.9%
p334850
 
5.2%
Other values (16)1558906
24.3%

ACOM_REP_parsed
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.9 MiB
Mayor de 18 aƱos no acompaƱado
270891 
NNA* no acompaƱado
 
13199
Mayor de 18 aƱos acompaƱado de un familiar
 
7671
NNA* acompaƱado de un familiar
 
5572
Mayor de 18 aƱos acompaƱado de un amigo o conocido
 
3733

Length

Max length50
Median length30
Mean length30.01981588
Min length15

Characters and Unicode

Total characters9042659
Distinct characters24
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMayor de 18 aƱos no acompaƱado
2nd rowMayor de 18 aƱos no acompaƱado
3rd rowMayor de 18 aƱos no acompaƱado
4th rowMayor de 18 aƱos no acompaƱado
5th rowMayor de 18 aƱos no acompaƱado

Common Values

ValueCountFrequency (%)
Mayor de 18 aƱos no acompaƱado270891
89.9%
NNA* no acompaƱado13199
 
4.4%
Mayor de 18 aƱos acompaƱado de un familiar7671
 
2.5%
NNA* acompaƱado de un familiar5572
 
1.8%
Mayor de 18 aƱos acompaƱado de un amigo o conocido3733
 
1.2%
No especificado157
 
0.1%

Length

2022-10-31T15:40:04.270897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:04.454897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
acompaƱado301066
16.8%
de299271
16.7%
no284247
15.9%
mayor282295
15.8%
18282295
15.8%
aƱos282295
15.8%
nna18771
 
1.0%
un16976
 
0.9%
familiar13243
 
0.7%
amigo3733
 
0.2%
Other values (3)7623
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a1498164
16.6%
1490592
16.5%
o1469791
16.3%
d604227
 
6.7%
Ʊ583361
 
6.5%
m318042
 
3.5%
c308846
 
3.4%
n304799
 
3.4%
p301223
 
3.3%
e299585
 
3.3%
Other values (14)1864029
20.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6629941
73.3%
Space Separator1490592
 
16.5%
Decimal Number564590
 
6.2%
Uppercase Letter338765
 
3.7%
Other Punctuation18771
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1498164
22.6%
o1469791
22.2%
d604227
9.1%
Ʊ583361
 
8.8%
m318042
 
4.8%
c308846
 
4.7%
n304799
 
4.6%
p301223
 
4.5%
e299585
 
4.5%
r295538
 
4.5%
Other values (7)646365
9.7%
Uppercase Letter
ValueCountFrequency (%)
M282295
83.3%
N37699
 
11.1%
A18771
 
5.5%
Decimal Number
ValueCountFrequency (%)
8282295
50.0%
1282295
50.0%
Space Separator
ValueCountFrequency (%)
1490592
100.0%
Other Punctuation
ValueCountFrequency (%)
*18771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6968706
77.1%
Common2073953
 
22.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1498164
21.5%
o1469791
21.1%
d604227
8.7%
Ʊ583361
 
8.4%
m318042
 
4.6%
c308846
 
4.4%
n304799
 
4.4%
p301223
 
4.3%
e299585
 
4.3%
r295538
 
4.2%
Other values (10)985130
14.1%
Common
ValueCountFrequency (%)
1490592
71.9%
8282295
 
13.6%
1282295
 
13.6%
*18771
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII8459298
93.5%
None583361
 
6.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1498164
17.7%
1490592
17.6%
o1469791
17.4%
d604227
 
7.1%
m318042
 
3.8%
c308846
 
3.7%
n304799
 
3.6%
p301223
 
3.6%
e299585
 
3.5%
r295538
 
3.5%
Other values (13)1568491
18.5%
None
ValueCountFrequency (%)
Ʊ583361
100.0%

PERM_EU_parsed
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.6 MiB
De 0 a 30 dĆ­as
217204 
1 a 6 meses
55786 
1 a 5 aƱos
 
7186
7 a 12 meses
 
6734
11 a 20 aƱos
 
5678
Other values (4)
 
8635

Length

Max length15
Median length14
Mean length13.210057
Min length10

Characters and Unicode

Total characters3979173
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDe 0 a 30 dĆ­as
2nd rowDe 0 a 30 dĆ­as
3rd rowDe 0 a 30 dĆ­as
4th rowDe 0 a 30 dĆ­as
5th row1 a 5 aƱos

Common Values

ValueCountFrequency (%)
De 0 a 30 dĆ­as217204
72.1%
1 a 6 meses55786
 
18.5%
1 a 5 aƱos7186
 
2.4%
7 a 12 meses6734
 
2.2%
11 a 20 aƱos5678
 
1.9%
6 a 10 aƱos4172
 
1.4%
21 a 40 aƱos2870
 
1.0%
No especificado1233
 
0.4%
MƔs de 40 aƱos360
 
0.1%

Length

2022-10-31T15:40:04.628898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:04.827501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
a299630
21.1%
de217564
15.3%
30217204
15.3%
dĆ­as217204
15.3%
0217204
15.3%
162972
 
4.4%
meses62520
 
4.4%
659958
 
4.2%
aƱos20266
 
1.4%
57186
 
0.5%
Other values (10)37922
 
2.7%

Most occurring characters

ValueCountFrequency (%)
1118407
28.1%
a538333
13.5%
0447488
11.2%
s364103
 
9.2%
e345070
 
8.7%
d218797
 
5.5%
D217204
 
5.5%
3217204
 
5.5%
Ć­217204
 
5.5%
188104
 
2.2%
Other values (15)207259
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1796783
45.2%
Space Separator1118407
28.1%
Decimal Number845186
21.2%
Uppercase Letter218797
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a538333
30.0%
s364103
20.3%
e345070
19.2%
d218797
12.2%
Ć­217204
12.1%
m62520
 
3.5%
o22732
 
1.3%
Ʊ20266
 
1.1%
c2466
 
0.1%
i2466
 
0.1%
Other values (3)2826
 
0.2%
Decimal Number
ValueCountFrequency (%)
0447488
52.9%
3217204
25.7%
188104
 
10.4%
659958
 
7.1%
215282
 
1.8%
57186
 
0.9%
76734
 
0.8%
43230
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
D217204
99.3%
N1233
 
0.6%
M360
 
0.2%
Space Separator
ValueCountFrequency (%)
1118407
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2015580
50.7%
Common1963593
49.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a538333
26.7%
s364103
18.1%
e345070
17.1%
d218797
10.9%
D217204
10.8%
Ć­217204
10.8%
m62520
 
3.1%
o22732
 
1.1%
Ʊ20266
 
1.0%
c2466
 
0.1%
Other values (6)6885
 
0.3%
Common
ValueCountFrequency (%)
1118407
57.0%
0447488
22.8%
3217204
 
11.1%
188104
 
4.5%
659958
 
3.1%
215282
 
0.8%
57186
 
0.4%
76734
 
0.3%
43230
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3741343
94.0%
None237830
 
6.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1118407
29.9%
a538333
14.4%
0447488
12.0%
s364103
 
9.7%
e345070
 
9.2%
d218797
 
5.8%
D217204
 
5.8%
3217204
 
5.8%
188104
 
2.4%
m62520
 
1.7%
Other values (12)124113
 
3.3%
None
ValueCountFrequency (%)
Ć­217204
91.3%
Ʊ20266
 
8.5%
Ɣ360
 
0.2%

EDO_DET_parsed
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.5 MiB
Arizona
229851 
California
47094 
Texas
 
17833
New Mexico
 
878
Arkansas
 
618
Other values (46)
 
4949

Length

Max length17
Median length7
Mean length7.382437596
Min length4

Characters and Unicode

Total characters2223760
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona229851
76.3%
California47094
 
15.6%
Texas17833
 
5.9%
New Mexico878
 
0.3%
Arkansas618
 
0.2%
Georgia450
 
0.1%
Florida389
 
0.1%
Alaska362
 
0.1%
Nevada331
 
0.1%
North Carolina295
 
0.1%
Other values (41)3122
 
1.0%

Length

2022-10-31T15:40:05.012501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
arizona229851
75.9%
california47094
 
15.5%
texas17833
 
5.9%
new1042
 
0.3%
mexico878
 
0.3%
arkansas618
 
0.2%
georgia450
 
0.1%
carolina440
 
0.1%
florida389
 
0.1%
alaska362
 
0.1%
Other values (44)3951
 
1.3%

Most occurring characters

ValueCountFrequency (%)
a349611
15.7%
i328693
14.8%
o281755
12.7%
n280349
12.6%
r279807
12.6%
A231088
10.4%
z229851
10.3%
l49214
 
2.2%
C47755
 
2.1%
f47254
 
2.1%
Other values (37)98383
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1919324
86.3%
Uppercase Letter302748
 
13.6%
Space Separator1685
 
0.1%
Other Punctuation3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a349611
18.2%
i328693
17.1%
o281755
14.7%
n280349
14.6%
r279807
14.6%
z229851
12.0%
l49214
 
2.6%
f47254
 
2.5%
e22183
 
1.2%
s21565
 
1.1%
Other values (14)29042
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
A231088
76.3%
C47755
 
15.8%
T18014
 
6.0%
N1927
 
0.6%
M1253
 
0.4%
G450
 
0.1%
F389
 
0.1%
I335
 
0.1%
O253
 
0.1%
W239
 
0.1%
Other values (10)1045
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.2
66.7%
,1
33.3%
Space Separator
ValueCountFrequency (%)
1685
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2222072
99.9%
Common1688
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a349611
15.7%
i328693
14.8%
o281755
12.7%
n280349
12.6%
r279807
12.6%
A231088
10.4%
z229851
10.3%
l49214
 
2.2%
C47755
 
2.1%
f47254
 
2.1%
Other values (34)96695
 
4.4%
Common
ValueCountFrequency (%)
1685
99.8%
.2
 
0.1%
,1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2223760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a349611
15.7%
i328693
14.8%
o281755
12.7%
n280349
12.6%
r279807
12.6%
A231088
10.4%
z229851
10.3%
l49214
 
2.2%
C47755
 
2.1%
f47254
 
2.1%
Other values (37)98383
 
4.4%

AUT_DEP_parsed
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.8 MiB
Border Patrol
148438 
ICE
74279 
CBP
63148 
No especificado
15282 
US Marshals
 
76

Length

Max length15
Median length13
Mean length8.538660726
Min length3

Characters and Unicode

Total characters2572041
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCBP
2nd rowCBP
3rd rowCBP
4th rowICE
5th rowICE

Common Values

ValueCountFrequency (%)
Border Patrol148438
49.3%
ICE74279
24.7%
CBP63148
21.0%
No especificado15282
 
5.1%
US Marshals76
 
< 0.1%

Length

2022-10-31T15:40:05.189502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:05.374502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
border148438
31.9%
patrol148438
31.9%
ice74279
16.0%
cbp63148
13.6%
no15282
 
3.3%
especificado15282
 
3.3%
us76
 
< 0.1%
marshals76
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r445390
17.3%
o327440
12.7%
B211586
8.2%
P211586
8.2%
e179002
7.0%
a163872
 
6.4%
163796
 
6.4%
d163720
 
6.4%
l148514
 
5.8%
t148438
 
5.8%
Other values (13)408697
15.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1683578
65.5%
Uppercase Letter724667
28.2%
Space Separator163796
 
6.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r445390
26.5%
o327440
19.4%
e179002
10.6%
a163872
 
9.7%
d163720
 
9.7%
l148514
 
8.8%
t148438
 
8.8%
c30564
 
1.8%
i30564
 
1.8%
s15434
 
0.9%
Other values (3)30640
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
B211586
29.2%
P211586
29.2%
C137427
19.0%
I74279
 
10.3%
E74279
 
10.3%
N15282
 
2.1%
U76
 
< 0.1%
S76
 
< 0.1%
M76
 
< 0.1%
Space Separator
ValueCountFrequency (%)
163796
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2408245
93.6%
Common163796
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r445390
18.5%
o327440
13.6%
B211586
8.8%
P211586
8.8%
e179002
7.4%
a163872
 
6.8%
d163720
 
6.8%
l148514
 
6.2%
t148438
 
6.2%
C137427
 
5.7%
Other values (12)271270
11.3%
Common
ValueCountFrequency (%)
163796
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2572041
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r445390
17.3%
o327440
12.7%
B211586
8.2%
P211586
8.2%
e179002
7.0%
a163872
 
6.4%
163796
 
6.4%
d163720
 
6.4%
l148514
 
5.8%
t148438
 
5.8%
Other values (13)408697
15.9%

EN_DES_parsed
Categorical

HIGH CORRELATION

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.7 MiB
Sonora
41610 
Baja California
35969 
Guerrero
25818 
Oaxaca
25068 
Chiapas
24238 
Other values (28)
148520 

Length

Max length19
Median length16
Mean length8.343038214
Min length6

Characters and Unicode

Total characters2513115
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJalisco
2nd rowPuebla
3rd rowGuerrero
4th rowJalisco
5th rowJalisco

Common Values

ValueCountFrequency (%)
Sonora41610
13.8%
Baja California35969
11.9%
Guerrero25818
 
8.6%
Oaxaca25068
 
8.3%
Chiapas24238
 
8.0%
Sinaloa22288
 
7.4%
Puebla20178
 
6.7%
MichoacƔn15191
 
5.0%
MƩxico12068
 
4.0%
Veracruz12016
 
4.0%
Other values (23)66779
22.2%

Length

2022-10-31T15:40:05.555502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sonora41610
11.7%
baja36352
 
10.2%
california36352
 
10.2%
guerrero25818
 
7.2%
oaxaca25068
 
7.0%
chiapas24238
 
6.8%
sinaloa22288
 
6.3%
puebla20178
 
5.7%
mƩxico17757
 
5.0%
michoacƔn15191
 
4.3%
Other values (30)91530
25.7%

Most occurring characters

ValueCountFrequency (%)
a506461
20.2%
o250083
 
10.0%
r195893
 
7.8%
i193437
 
7.7%
n134167
 
5.3%
u108310
 
4.3%
l107678
 
4.3%
e104915
 
4.2%
c103254
 
4.1%
C75078
 
3.0%
Other values (37)733839
29.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2107330
83.9%
Uppercase Letter350626
 
14.0%
Space Separator55159
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a506461
24.0%
o250083
11.9%
r195893
 
9.3%
i193437
 
9.2%
n134167
 
6.4%
u108310
 
5.1%
l107678
 
5.1%
e104915
 
5.0%
c103254
 
4.9%
h59187
 
2.8%
Other values (17)343945
16.3%
Uppercase Letter
ValueCountFrequency (%)
C75078
21.4%
S66537
19.0%
M36706
10.5%
B36352
10.4%
G33740
9.6%
O25068
 
7.1%
P22434
 
6.4%
V12016
 
3.4%
J8390
 
2.4%
H8221
 
2.3%
Other values (9)26084
 
7.4%
Space Separator
ValueCountFrequency (%)
55159
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2457956
97.8%
Common55159
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a506461
20.6%
o250083
 
10.2%
r195893
 
8.0%
i193437
 
7.9%
n134167
 
5.5%
u108310
 
4.4%
l107678
 
4.4%
e104915
 
4.3%
c103254
 
4.2%
C75078
 
3.1%
Other values (36)678680
27.6%
Common
ValueCountFrequency (%)
55159
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2473254
98.4%
None39861
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a506461
20.5%
o250083
 
10.1%
r195893
 
7.9%
i193437
 
7.8%
n134167
 
5.4%
u108310
 
4.4%
l107678
 
4.4%
e104915
 
4.2%
c103254
 
4.2%
C75078
 
3.0%
Other values (33)693978
28.1%
None
ValueCountFrequency (%)
Ʃ20363
51.1%
Ɣ15732
39.5%
Ć­2256
 
5.7%
ó1510
 
3.8%

CAN_AL_parsed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.2 MiB
No
158193 
SĆ­
143030 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters602446
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSĆ­
2nd rowSĆ­
3rd rowSĆ­
4th rowSĆ­
5th rowSĆ­

Common Values

ValueCountFrequency (%)
No158193
52.5%
SĆ­143030
47.5%

Length

2022-10-31T15:40:05.725502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:05.889501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no158193
52.5%
sĆ­143030
47.5%

Most occurring characters

ValueCountFrequency (%)
N158193
26.3%
o158193
26.3%
S143030
23.7%
Ć­143030
23.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter301223
50.0%
Lowercase Letter301223
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N158193
52.5%
S143030
47.5%
Lowercase Letter
ValueCountFrequency (%)
o158193
52.5%
Ć­143030
47.5%

Most occurring scripts

ValueCountFrequency (%)
Latin602446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N158193
26.3%
o158193
26.3%
S143030
23.7%
Ć­143030
23.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII459416
76.3%
None143030
 
23.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N158193
34.4%
o158193
34.4%
S143030
31.1%
None
ValueCountFrequency (%)
Ć­143030
100.0%

CAN_COM_parsed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.9 MiB
No
173520 
SĆ­
127703 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters602446
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSĆ­
2nd rowSĆ­
3rd rowSĆ­
4th rowSĆ­
5th rowSĆ­

Common Values

ValueCountFrequency (%)
No173520
57.6%
SĆ­127703
42.4%

Length

2022-10-31T15:40:06.040502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:06.206517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no173520
57.6%
sĆ­127703
42.4%

Most occurring characters

ValueCountFrequency (%)
N173520
28.8%
o173520
28.8%
S127703
21.2%
Ć­127703
21.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter301223
50.0%
Lowercase Letter301223
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N173520
57.6%
S127703
42.4%
Lowercase Letter
ValueCountFrequency (%)
o173520
57.6%
Ć­127703
42.4%

Most occurring scripts

ValueCountFrequency (%)
Latin602446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N173520
28.8%
o173520
28.8%
S127703
21.2%
Ć­127703
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII474743
78.8%
None127703
 
21.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N173520
36.6%
o173520
36.6%
S127703
26.9%
None
ValueCountFrequency (%)
Ć­127703
100.0%

CAN_DIF_parsed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.3 MiB
No
286079 
SĆ­
 
15144

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters602446
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No286079
95.0%
SĆ­15144
 
5.0%

Length

2022-10-31T15:40:06.361070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:06.526703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no286079
95.0%
sĆ­15144
 
5.0%

Most occurring characters

ValueCountFrequency (%)
N286079
47.5%
o286079
47.5%
S15144
 
2.5%
Ć­15144
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter301223
50.0%
Lowercase Letter301223
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N286079
95.0%
S15144
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
o286079
95.0%
Ć­15144
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Latin602446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N286079
47.5%
o286079
47.5%
S15144
 
2.5%
Ć­15144
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII587302
97.5%
None15144
 
2.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N286079
48.7%
o286079
48.7%
S15144
 
2.6%
None
ValueCountFrequency (%)
Ć­15144
100.0%

CAN_HOS_parsed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.1 MiB
No
293038 
SĆ­
 
8185

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters602446
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No293038
97.3%
SĆ­8185
 
2.7%

Length

2022-10-31T15:40:06.676752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:07.016570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no293038
97.3%
sĆ­8185
 
2.7%

Most occurring characters

ValueCountFrequency (%)
N293038
48.6%
o293038
48.6%
S8185
 
1.4%
Ć­8185
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter301223
50.0%
Lowercase Letter301223
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N293038
97.3%
S8185
 
2.7%
Lowercase Letter
ValueCountFrequency (%)
o293038
97.3%
Ć­8185
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin602446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N293038
48.6%
o293038
48.6%
S8185
 
1.4%
Ć­8185
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII594261
98.6%
None8185
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N293038
49.3%
o293038
49.3%
S8185
 
1.4%
None
ValueCountFrequency (%)
Ć­8185
100.0%

CAN_STRA_parsed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.6 MiB
No
272940 
SĆ­
28283 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters602446
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No272940
90.6%
SĆ­28283
 
9.4%

Length

2022-10-31T15:40:07.160570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:07.322595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no272940
90.6%
sĆ­28283
 
9.4%

Most occurring characters

ValueCountFrequency (%)
N272940
45.3%
o272940
45.3%
S28283
 
4.7%
Ć­28283
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter301223
50.0%
Lowercase Letter301223
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N272940
90.6%
S28283
 
9.4%
Lowercase Letter
ValueCountFrequency (%)
o272940
90.6%
Ć­28283
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
Latin602446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N272940
45.3%
o272940
45.3%
S28283
 
4.7%
Ć­28283
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII574163
95.3%
None28283
 
4.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N272940
47.5%
o272940
47.5%
S28283
 
4.9%
None
ValueCountFrequency (%)
Ć­28283
100.0%

CAN_SEGPOP_parsed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.1 MiB
No
249626 
SĆ­
51597 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters602446
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No249626
82.9%
SĆ­51597
 
17.1%

Length

2022-10-31T15:40:07.474626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:07.639626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no249626
82.9%
sĆ­51597
 
17.1%

Most occurring characters

ValueCountFrequency (%)
N249626
41.4%
o249626
41.4%
S51597
 
8.6%
Ć­51597
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter301223
50.0%
Lowercase Letter301223
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N249626
82.9%
S51597
 
17.1%
Lowercase Letter
ValueCountFrequency (%)
o249626
82.9%
Ć­51597
 
17.1%

Most occurring scripts

ValueCountFrequency (%)
Latin602446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N249626
41.4%
o249626
41.4%
S51597
 
8.6%
Ć­51597
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII550849
91.4%
None51597
 
8.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N249626
45.3%
o249626
45.3%
S51597
 
9.4%
None
ValueCountFrequency (%)
Ć­51597
100.0%

CAN_OFAM_parsed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.5 MiB
No
275524 
SĆ­
 
25699

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters602446
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No275524
91.5%
SĆ­25699
 
8.5%

Length

2022-10-31T15:40:07.789626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:07.955292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no275524
91.5%
sĆ­25699
 
8.5%

Most occurring characters

ValueCountFrequency (%)
N275524
45.7%
o275524
45.7%
S25699
 
4.3%
Ć­25699
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter301223
50.0%
Lowercase Letter301223
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N275524
91.5%
S25699
 
8.5%
Lowercase Letter
ValueCountFrequency (%)
o275524
91.5%
Ć­25699
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Latin602446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N275524
45.7%
o275524
45.7%
S25699
 
4.3%
Ć­25699
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII576747
95.7%
None25699
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N275524
47.8%
o275524
47.8%
S25699
 
4.5%
None
ValueCountFrequency (%)
Ć­25699
100.0%

AGUA_AL_parsed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.7 MiB
SĆ­
250502 
No
50721 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters602446
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSĆ­
2nd rowSĆ­
3rd rowSĆ­
4th rowSĆ­
5th rowSĆ­

Common Values

ValueCountFrequency (%)
SĆ­250502
83.2%
No50721
 
16.8%

Length

2022-10-31T15:40:08.100362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:08.264535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
sĆ­250502
83.2%
no50721
 
16.8%

Most occurring characters

ValueCountFrequency (%)
S250502
41.6%
Ć­250502
41.6%
N50721
 
8.4%
o50721
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter301223
50.0%
Lowercase Letter301223
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S250502
83.2%
N50721
 
16.8%
Lowercase Letter
ValueCountFrequency (%)
Ć­250502
83.2%
o50721
 
16.8%

Most occurring scripts

ValueCountFrequency (%)
Latin602446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S250502
41.6%
Ć­250502
41.6%
N50721
 
8.4%
o50721
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII351944
58.4%
None250502
41.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S250502
71.2%
N50721
 
14.4%
o50721
 
14.4%
None
ValueCountFrequency (%)
Ć­250502
100.0%

DESC_BUS_parsed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.3 MiB
No
153377 
SĆ­
147846 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters602446
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSĆ­
2nd rowSĆ­
3rd rowSĆ­
4th rowSĆ­
5th rowSĆ­

Common Values

ValueCountFrequency (%)
No153377
50.9%
SĆ­147846
49.1%

Length

2022-10-31T15:40:08.416553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:08.582582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no153377
50.9%
sĆ­147846
49.1%

Most occurring characters

ValueCountFrequency (%)
N153377
25.5%
o153377
25.5%
S147846
24.5%
Ć­147846
24.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter301223
50.0%
Lowercase Letter301223
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N153377
50.9%
S147846
49.1%
Lowercase Letter
ValueCountFrequency (%)
o153377
50.9%
Ć­147846
49.1%

Most occurring scripts

ValueCountFrequency (%)
Latin602446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N153377
25.5%
o153377
25.5%
S147846
24.5%
Ć­147846
24.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII454600
75.5%
None147846
 
24.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N153377
33.7%
o153377
33.7%
S147846
32.5%
None
ValueCountFrequency (%)
Ć­147846
100.0%

APO_AUX_parsed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.7 MiB
No
269197 
SĆ­
32026 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters602446
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No269197
89.4%
SĆ­32026
 
10.6%

Length

2022-10-31T15:40:08.739567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:08.919507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no269197
89.4%
sĆ­32026
 
10.6%

Most occurring characters

ValueCountFrequency (%)
N269197
44.7%
o269197
44.7%
S32026
 
5.3%
Ć­32026
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter301223
50.0%
Lowercase Letter301223
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N269197
89.4%
S32026
 
10.6%
Lowercase Letter
ValueCountFrequency (%)
o269197
89.4%
Ć­32026
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Latin602446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N269197
44.7%
o269197
44.7%
S32026
 
5.3%
Ć­32026
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII570420
94.7%
None32026
 
5.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N269197
47.2%
o269197
47.2%
S32026
 
5.6%
None
ValueCountFrequency (%)
Ć­32026
100.0%

APO_LLAM_parsed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.3 MiB
No
156627 
SĆ­
144596 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters602446
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSĆ­
2nd rowSĆ­
3rd rowSĆ­
4th rowSĆ­
5th rowSĆ­

Common Values

ValueCountFrequency (%)
No156627
52.0%
SĆ­144596
48.0%

Length

2022-10-31T15:40:09.071560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:09.241605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no156627
52.0%
sĆ­144596
48.0%

Most occurring characters

ValueCountFrequency (%)
N156627
26.0%
o156627
26.0%
S144596
24.0%
Ć­144596
24.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter301223
50.0%
Lowercase Letter301223
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N156627
52.0%
S144596
48.0%
Lowercase Letter
ValueCountFrequency (%)
o156627
52.0%
Ć­144596
48.0%

Most occurring scripts

ValueCountFrequency (%)
Latin602446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N156627
26.0%
o156627
26.0%
S144596
24.0%
Ć­144596
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII457850
76.0%
None144596
 
24.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N156627
34.2%
o156627
34.2%
S144596
31.6%
None
ValueCountFrequency (%)
Ć­144596
100.0%

APO_MAT_parsed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.3 MiB
No
197769 
SĆ­
103454 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters602446
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No197769
65.7%
SĆ­103454
34.3%

Length

2022-10-31T15:40:09.391705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:09.579752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no197769
65.7%
sĆ­103454
34.3%

Most occurring characters

ValueCountFrequency (%)
N197769
32.8%
o197769
32.8%
S103454
17.2%
Ć­103454
17.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter301223
50.0%
Lowercase Letter301223
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N197769
65.7%
S103454
34.3%
Lowercase Letter
ValueCountFrequency (%)
o197769
65.7%
Ć­103454
34.3%

Most occurring scripts

ValueCountFrequency (%)
Latin602446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N197769
32.8%
o197769
32.8%
S103454
17.2%
Ć­103454
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII498992
82.8%
None103454
 
17.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N197769
39.6%
o197769
39.6%
S103454
20.7%
None
ValueCountFrequency (%)
Ć­103454
100.0%

APO_TRAS_parsed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.3 MiB
No
198588 
SĆ­
102635 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters602446
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No198588
65.9%
SĆ­102635
34.1%

Length

2022-10-31T15:40:09.736796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:09.908899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no198588
65.9%
sĆ­102635
34.1%

Most occurring characters

ValueCountFrequency (%)
N198588
33.0%
o198588
33.0%
S102635
17.0%
Ć­102635
17.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter301223
50.0%
Lowercase Letter301223
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N198588
65.9%
S102635
34.1%
Lowercase Letter
ValueCountFrequency (%)
o198588
65.9%
Ć­102635
34.1%

Most occurring scripts

ValueCountFrequency (%)
Latin602446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N198588
33.0%
o198588
33.0%
S102635
17.0%
Ć­102635
17.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII499811
83.0%
None102635
 
17.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N198588
39.7%
o198588
39.7%
S102635
20.5%
None
ValueCountFrequency (%)
Ć­102635
100.0%

APO_VES_parsed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.8 MiB
No
264478 
SĆ­
36745 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters602446
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No264478
87.8%
SĆ­36745
 
12.2%

Length

2022-10-31T15:40:10.062192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:10.228235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no264478
87.8%
sĆ­36745
 
12.2%

Most occurring characters

ValueCountFrequency (%)
N264478
43.9%
o264478
43.9%
S36745
 
6.1%
Ć­36745
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter301223
50.0%
Lowercase Letter301223
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N264478
87.8%
S36745
 
12.2%
Lowercase Letter
ValueCountFrequency (%)
o264478
87.8%
Ć­36745
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Latin602446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N264478
43.9%
o264478
43.9%
S36745
 
6.1%
Ć­36745
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII565701
93.9%
None36745
 
6.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N264478
46.8%
o264478
46.8%
S36745
 
6.5%
None
ValueCountFrequency (%)
Ć­36745
100.0%

APO_ACT_NAC_parsed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.6 MiB
No
273978 
SĆ­
 
27245

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters602446
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No273978
91.0%
SĆ­27245
 
9.0%

Length

2022-10-31T15:40:10.379234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:10.553234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no273978
91.0%
sĆ­27245
 
9.0%

Most occurring characters

ValueCountFrequency (%)
N273978
45.5%
o273978
45.5%
S27245
 
4.5%
Ć­27245
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter301223
50.0%
Lowercase Letter301223
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N273978
91.0%
S27245
 
9.0%
Lowercase Letter
ValueCountFrequency (%)
o273978
91.0%
Ć­27245
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Latin602446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N273978
45.5%
o273978
45.5%
S27245
 
4.5%
Ć­27245
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII575201
95.5%
None27245
 
4.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N273978
47.6%
o273978
47.6%
S27245
 
4.7%
None
ValueCountFrequency (%)
Ć­27245
100.0%

APO_TRASF_parsed
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing301223
Missing (%)100.0%
Memory size2.3 MiB

APO_CURP_parsed
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing67167
Missing (%)22.3%
Memory size16.0 MiB
No
198501 
SĆ­
35555 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters468112
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No198501
65.9%
SĆ­35555
 
11.8%
(Missing)67167
 
22.3%

Length

2022-10-31T15:40:10.706235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:10.867231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no198501
84.8%
sĆ­35555
 
15.2%

Most occurring characters

ValueCountFrequency (%)
N198501
42.4%
o198501
42.4%
S35555
 
7.6%
Ć­35555
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter234056
50.0%
Lowercase Letter234056
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N198501
84.8%
S35555
 
15.2%
Lowercase Letter
ValueCountFrequency (%)
o198501
84.8%
Ć­35555
 
15.2%

Most occurring scripts

ValueCountFrequency (%)
Latin468112
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N198501
42.4%
o198501
42.4%
S35555
 
7.6%
Ć­35555
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII432557
92.4%
None35555
 
7.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N198501
45.9%
o198501
45.9%
S35555
 
8.2%
None
ValueCountFrequency (%)
Ć­35555
100.0%

APO_ASF_parsed
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing67167
Missing (%)22.3%
Memory size17.0 MiB
No
155973 
SĆ­
78083 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters468112
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No155973
51.8%
SĆ­78083
25.9%
(Missing)67167
22.3%

Length

2022-10-31T15:40:11.025002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:11.185005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no155973
66.6%
sĆ­78083
33.4%

Most occurring characters

ValueCountFrequency (%)
N155973
33.3%
o155973
33.3%
S78083
16.7%
Ć­78083
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter234056
50.0%
Lowercase Letter234056
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N155973
66.6%
S78083
33.4%
Lowercase Letter
ValueCountFrequency (%)
o155973
66.6%
Ć­78083
33.4%

Most occurring scripts

ValueCountFrequency (%)
Latin468112
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N155973
33.3%
o155973
33.3%
S78083
16.7%
Ć­78083
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII390029
83.3%
None78083
 
16.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N155973
40.0%
o155973
40.0%
S78083
20.0%
None
ValueCountFrequency (%)
Ć­78083
100.0%

APO_AME_parsed
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing67167
Missing (%)22.3%
Memory size16.7 MiB
No
170236 
SĆ­
63820 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters468112
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No170236
56.5%
SĆ­63820
 
21.2%
(Missing)67167
 
22.3%

Length

2022-10-31T15:40:11.514966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:11.678379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no170236
72.7%
sĆ­63820
 
27.3%

Most occurring characters

ValueCountFrequency (%)
N170236
36.4%
o170236
36.4%
S63820
 
13.6%
Ć­63820
 
13.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter234056
50.0%
Lowercase Letter234056
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N170236
72.7%
S63820
 
27.3%
Lowercase Letter
ValueCountFrequency (%)
o170236
72.7%
Ć­63820
 
27.3%

Most occurring scripts

ValueCountFrequency (%)
Latin468112
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N170236
36.4%
o170236
36.4%
S63820
 
13.6%
Ć­63820
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII404292
86.4%
None63820
 
13.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N170236
42.1%
o170236
42.1%
S63820
 
15.8%
None
ValueCountFrequency (%)
Ć­63820
100.0%

APO_REC_PERT_parsed
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing67167
Missing (%)22.3%
Memory size15.8 MiB
No
207231 
SĆ­
26825 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters468112
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No207231
68.8%
SĆ­26825
 
8.9%
(Missing)67167
 
22.3%

Length

2022-10-31T15:40:11.818379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:11.970381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no207231
88.5%
sĆ­26825
 
11.5%

Most occurring characters

ValueCountFrequency (%)
N207231
44.3%
o207231
44.3%
S26825
 
5.7%
Ć­26825
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter234056
50.0%
Lowercase Letter234056
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N207231
88.5%
S26825
 
11.5%
Lowercase Letter
ValueCountFrequency (%)
o207231
88.5%
Ć­26825
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
Latin468112
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N207231
44.3%
o207231
44.3%
S26825
 
5.7%
Ć­26825
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII441287
94.3%
None26825
 
5.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N207231
47.0%
o207231
47.0%
S26825
 
6.1%
None
ValueCountFrequency (%)
Ć­26825
100.0%

APO_AT_MEDICA_parsed
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing67167
Missing (%)22.3%
Memory size16.3 MiB
No
186069 
SĆ­
47987 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters468112
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No186069
61.8%
SĆ­47987
 
15.9%
(Missing)67167
 
22.3%

Length

2022-10-31T15:40:12.110380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-31T15:40:12.274281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no186069
79.5%
sĆ­47987
 
20.5%

Most occurring characters

ValueCountFrequency (%)
N186069
39.7%
o186069
39.7%
S47987
 
10.3%
Ć­47987
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter234056
50.0%
Lowercase Letter234056
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N186069
79.5%
S47987
 
20.5%
Lowercase Letter
ValueCountFrequency (%)
o186069
79.5%
Ć­47987
 
20.5%

Most occurring scripts

ValueCountFrequency (%)
Latin468112
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N186069
39.7%
o186069
39.7%
S47987
 
10.3%
Ć­47987
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII420125
89.7%
None47987
 
10.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N186069
44.3%
o186069
44.3%
S47987
 
11.4%
None
ValueCountFrequency (%)
Ć­47987
100.0%

MUN_NAC_parsed
Categorical

HIGH CARDINALITY
MISSING

Distinct2164
Distinct (%)2.0%
Missing193153
Missing (%)64.1%
Memory size14.0 MiB
No especificado
38786 
Oaxaca de JuƔrez, Oax.
 
1590
CuliacƔn, Sin.
 
1348
Nogales, Son.
 
1191
Puebla, Pue.
 
955
Other values (2159)
64200 

Length

Max length82
Median length55
Mean length17.02336449
Min length10

Characters and Unicode

Total characters1839715
Distinct characters66
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique234 ?
Unique (%)0.2%

Sample

1st rowAguascalientes, Ags.
2nd rowAguascalientes, Ags.
3rd rowAguascalientes, Ags.
4th rowAguascalientes, Ags.
5th rowAguascalientes, Ags.

Common Values

ValueCountFrequency (%)
No especificado38786
 
12.9%
Oaxaca de JuƔrez, Oax.1590
 
0.5%
CuliacƔn, Sin.1348
 
0.4%
Nogales, Son.1191
 
0.4%
Puebla, Pue.955
 
0.3%
Tlapa de Comonfort, Gro.753
 
0.2%
Guasave, Sin.687
 
0.2%
San Luis RĆ­o Colorado, Son.685
 
0.2%
Acapulco de JuƔrez, Gro.594
 
0.2%
Morelia, Mich.590
 
0.2%
Other values (2154)60891
 
20.2%
(Missing)193153
64.1%

Length

2022-10-31T15:40:12.454281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no38786
 
14.5%
especificado38786
 
14.5%
de11798
 
4.4%
chis7361
 
2.7%
gro6283
 
2.3%
oax5906
 
2.2%
pue5656
 
2.1%
son5650
 
2.1%
san4896
 
1.8%
mich4869
 
1.8%
Other values (1980)138149
51.5%

Most occurring characters

ValueCountFrequency (%)
a173571
 
9.4%
160070
 
8.7%
o158287
 
8.6%
e152915
 
8.3%
i132042
 
7.2%
c118351
 
6.4%
.69893
 
3.8%
,69305
 
3.8%
s68101
 
3.7%
d61073
 
3.3%
Other values (56)676107
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1318824
71.7%
Uppercase Letter221463
 
12.0%
Space Separator160070
 
8.7%
Other Punctuation139198
 
7.6%
Dash Punctuation80
 
< 0.1%
Decimal Number80
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a173571
13.2%
o158287
12.0%
e152915
11.6%
i132042
10.0%
c118351
9.0%
s68101
 
5.2%
d61073
 
4.6%
p57478
 
4.4%
n55131
 
4.2%
l53586
 
4.1%
Other values (22)288289
21.9%
Uppercase Letter
ValueCountFrequency (%)
N43810
19.8%
C26994
12.2%
S21550
9.7%
M18484
8.3%
G13880
 
6.3%
P12963
 
5.9%
T12493
 
5.6%
A11135
 
5.0%
O9797
 
4.4%
J7766
 
3.5%
Other values (17)42591
19.2%
Decimal Number
ValueCountFrequency (%)
260
75.0%
010
 
12.5%
810
 
12.5%
Other Punctuation
ValueCountFrequency (%)
.69893
50.2%
,69305
49.8%
Space Separator
ValueCountFrequency (%)
160070
100.0%
Dash Punctuation
ValueCountFrequency (%)
-80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1540287
83.7%
Common299428
 
16.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a173571
 
11.3%
o158287
 
10.3%
e152915
 
9.9%
i132042
 
8.6%
c118351
 
7.7%
s68101
 
4.4%
d61073
 
4.0%
p57478
 
3.7%
n55131
 
3.6%
l53586
 
3.5%
Other values (49)509752
33.1%
Common
ValueCountFrequency (%)
160070
53.5%
.69893
23.3%
,69305
23.1%
-80
 
< 0.1%
260
 
< 0.1%
010
 
< 0.1%
810
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1817755
98.8%
None21960
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a173571
 
9.5%
160070
 
8.8%
o158287
 
8.7%
e152915
 
8.4%
i132042
 
7.3%
c118351
 
6.5%
.69893
 
3.8%
,69305
 
3.8%
s68101
 
3.7%
d61073
 
3.4%
Other values (47)654147
36.0%
None
ValueCountFrequency (%)
Ɣ12293
56.0%
Ć­3829
 
17.4%
ó2753
 
12.5%
Ʃ1984
 
9.0%
Ʊ476
 
2.2%
Ćŗ412
 
1.9%
Ɓ202
 
0.9%
ü7
 
< 0.1%
Ƒ4
 
< 0.1%

MUN_DES_parsed
Categorical

HIGH CARDINALITY

Distinct2288
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size22.6 MiB
No especificado
80219 
Tijuana, BC.
 
14449
Oaxaca de JuƔrez, Oax.
 
7581
Mexicali, BC.
 
7162
Nogales, Son.
 
6700
Other values (2283)
185112 

Length

Max length82
Median length46
Mean length16.92559997
Min length9

Characters and Unicode

Total characters5098380
Distinct characters66
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)< 0.1%

Sample

1st rowPuerto Vallarta, Jal.
2nd rowPuebla, Pue.
3rd rowAtenango del RĆ­o, Gro.
4th rowGuadalajara, Jal.
5th rowYahualica de GonzƔlez Gallo, Jal.

Common Values

ValueCountFrequency (%)
No especificado80219
26.6%
Tijuana, BC.14449
 
4.8%
Oaxaca de JuƔrez, Oax.7581
 
2.5%
Mexicali, BC.7162
 
2.4%
Nogales, Son.6700
 
2.2%
CuliacƔn, Sin.5191
 
1.7%
San Luis RĆ­o Colorado, Son.4965
 
1.6%
Agua Prieta, Son.3571
 
1.2%
Hermosillo, Son.3404
 
1.1%
Caborca, Son.2644
 
0.9%
Other values (2278)165337
54.9%

Length

2022-10-31T15:40:12.694249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no80219
 
10.6%
especificado80219
 
10.6%
de33565
 
4.4%
son33296
 
4.4%
bc23905
 
3.2%
gro19323
 
2.6%
oax18006
 
2.4%
sin16612
 
2.2%
chis15659
 
2.1%
san14572
 
1.9%
Other values (2080)422030
55.7%

Most occurring characters

ValueCountFrequency (%)
a505850
 
9.9%
456183
 
8.9%
o413116
 
8.1%
e381727
 
7.5%
i334625
 
6.6%
c277609
 
5.4%
.223270
 
4.4%
,221068
 
4.3%
n182560
 
3.6%
l167094
 
3.3%
Other values (56)1935278
38.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3521814
69.1%
Uppercase Letter675521
 
13.2%
Space Separator456183
 
8.9%
Other Punctuation444338
 
8.7%
Dash Punctuation262
 
< 0.1%
Decimal Number262
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a505850
14.4%
o413116
11.7%
e381727
10.8%
i334625
9.5%
c277609
 
7.9%
n182560
 
5.2%
l167094
 
4.7%
s161678
 
4.6%
d144070
 
4.1%
r142419
 
4.0%
Other values (22)811066
23.0%
Uppercase Letter
ValueCountFrequency (%)
C99089
14.7%
N98451
14.6%
S79154
11.7%
M53352
7.9%
T43466
 
6.4%
G40832
 
6.0%
P36285
 
5.4%
A31830
 
4.7%
B30863
 
4.6%
O30582
 
4.5%
Other values (17)131617
19.5%
Decimal Number
ValueCountFrequency (%)
2208
79.4%
027
 
10.3%
827
 
10.3%
Other Punctuation
ValueCountFrequency (%)
.223270
50.2%
,221068
49.8%
Space Separator
ValueCountFrequency (%)
456183
100.0%
Dash Punctuation
ValueCountFrequency (%)
-262
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4197335
82.3%
Common901045
 
17.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a505850
 
12.1%
o413116
 
9.8%
e381727
 
9.1%
i334625
 
8.0%
c277609
 
6.6%
n182560
 
4.3%
l167094
 
4.0%
s161678
 
3.9%
d144070
 
3.4%
r142419
 
3.4%
Other values (49)1486587
35.4%
Common
ValueCountFrequency (%)
456183
50.6%
.223270
24.8%
,221068
24.5%
-262
 
< 0.1%
2208
 
< 0.1%
027
 
< 0.1%
827
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5032777
98.7%
None65603
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a505850
 
10.1%
456183
 
9.1%
o413116
 
8.2%
e381727
 
7.6%
i334625
 
6.6%
c277609
 
5.5%
.223270
 
4.4%
,221068
 
4.4%
n182560
 
3.6%
l167094
 
3.3%
Other values (47)1869675
37.1%
None
ValueCountFrequency (%)
Ɣ37611
57.3%
Ć­13694
 
20.9%
ó6086
 
9.3%
Ʃ5263
 
8.0%
Ʊ1535
 
2.3%
Ćŗ890
 
1.4%
Ɓ494
 
0.8%
Ƒ18
 
< 0.1%
ü12
 
< 0.1%

Interactions

2022-10-31T15:39:32.622892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:05.184246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:08.015046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:11.306015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:13.876496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:16.667658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-31T15:39:21.693669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:24.838128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-31T15:39:19.409698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:22.089454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:25.074529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-31T15:39:30.266644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:33.011746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-31T15:39:08.471004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-31T15:39:14.353494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:17.121658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-31T15:39:22.313453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:25.302530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-31T15:39:08.703041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:12.027016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:14.618495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:17.350652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:19.867740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:22.689451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:25.539535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:28.073539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:30.739736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:33.389749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:06.348003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:08.933042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:12.256016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:14.876494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:17.579617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:20.101053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:23.040457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:25.785537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:28.460576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:30.981155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:33.578742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:06.585996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:09.244002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:12.481015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-31T15:39:26.260576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:28.908579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:31.449195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:34.121051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:07.052000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:09.840001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:12.971067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:15.607771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:18.286742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:20.800016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:23.896529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:26.484537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:29.135735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:31.725660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:34.323051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:07.290996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:10.585991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:13.211496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:15.846808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:18.515733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:21.025242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:24.153533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:26.717540image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:29.361735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:31.977816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:34.521049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:07.551518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:10.855990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:13.441496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:16.086807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:18.752737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:21.252664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:24.395551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:26.946575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:29.595772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:32.221274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:34.720054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:07.771051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:11.061991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:13.639496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:16.438811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:18.945732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:21.445665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:24.595553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:27.140577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:29.792997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-31T15:39:32.419887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-31T15:40:12.979282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-31T15:40:13.380282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-31T15:40:13.760286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's Ļ„

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (Ļ„) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate Ļ„ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. Ļ„ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-31T15:40:14.178529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-31T15:39:40.751274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-31T15:39:46.424649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-31T15:39:48.968943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0DELFEC_REPCLASIF_REPIRESEXOEDAEN_NACNIV_ESCACOM_REPPERM_EUEDO_DETAUT_DEPEN_DESMUN_DESCAN_ALCAN_COMCAN_DIFCAN_HOSCAN_STRACAN_SEGPOPCAN_OFAMAGUA_ALDESC_BUSAPO_AUXAPO_LLAMAPO_MATAPO_TRASAPO_VESAPO_ACT_NACAPO_TRASFAPO_CURPAPO_ASFAPO_AMEAPO_REC_PERTAPO_AT_MEDICAMUN_NACDEL_parsedFEC_REP_parsedCLASIF_REP_parsedIRE_parsedSEXO_parsedEDA_parsedEN_NAC_parsedNIV_ESC_parsedACOM_REP_parsedPERM_EU_parsedEDO_DET_parsedAUT_DEP_parsedEN_DES_parsedCAN_AL_parsedCAN_COM_parsedCAN_DIF_parsedCAN_HOS_parsedCAN_STRA_parsedCAN_SEGPOP_parsedCAN_OFAM_parsedAGUA_AL_parsedDESC_BUS_parsedAPO_AUX_parsedAPO_LLAM_parsedAPO_MAT_parsedAPO_TRAS_parsedAPO_VES_parsedAPO_ACT_NAC_parsedAPO_TRASF_parsedAPO_CURP_parsedAPO_ASF_parsedAPO_AME_parsedAPO_REC_PERT_parsedAPO_AT_MEDICA_parsedMUN_NAC_parsedMUN_DES_parsed
00262016-06-061160000011100147314214140671100000110100000.0NaNNaNNaNNaNNaNNaNSonoraNaNRepatriado de no reincidenciaNaNHombreNaNJaliscoPreparatoria o bachillerato CompletoMayor de 18 aƱos no acompaƱadoDe 0 a 30 dƭasArizonaCBPJaliscoSƭSƭNoNoNoNoNoSƭSƭNoSƭNoNoNoNoNaNNaNNaNNaNNaNNaNNaNPuerto Vallarta, Jal.
11262016-01-22116000002199215314221211141100000110100000.0NaNNaNNaNNaNNaNNaNSonoraNaNRepatriado de no reincidenciaNaNHombreNaNPueblaSecundaria CompletaMayor de 18 aƱos no acompaƱadoDe 0 a 30 dƭasArizonaCBPPueblaSƭSƭNoNoNoNoNoSƭSƭNoSƭNoNoNoNoNaNNaNNaNNaNNaNNaNNaNPuebla, Pue.
22262016-11-101160000031100127314212120081100000110100000.0NaNNaNNaNNaNNaNNaNSonoraNaNRepatriado de no reincidenciaNaNHombreNaNGuerreroPreparatoria o bachillerato CompletoMayor de 18 aƱos no acompaƱadoDe 0 a 30 dƭasArizonaCBPGuerreroSƭSƭNoNoNoNoNoSƭSƭNoSƭNoNoNoNoNaNNaNNaNNaNNaNNaNNaNAtenango del Rƭo, Gro.
33262016-07-10116000007188142314314140391100000110100000.0NaNNaNNaNNaNNaNNaNSonoraNaNRepatriado de no reincidenciaNaNHombreNaNJaliscoPrimaria IncompletaMayor de 18 aƱos no acompaƱadoDe 0 a 30 dƭasArizonaICEJaliscoSƭSƭNoNoNoNoNoSƭSƭNoSƭNoNoNoNoNaNNaNNaNNaNNaNNaNNaNGuadalajara, Jal.
44262016-06-03116000010183142344314141181100000110100000.0NaNNaNNaNNaNNaNNaNSonoraNaNRepatriado de no reincidenciaNaNHombreNaNJaliscoPrimaria IncompletaMayor de 18 aƱos no acompaƱado1 a 5 aƱosArizonaICEJaliscoSƭSƭNoNoNoNoNoSƭSƭNoSƭNoNoNoNoNaNNaNNaNNaNNaNNaNNaNYahualica de GonzƔlez Gallo, Jal.
55262016-11-24116000013183205324320200671100000110100000.0NaNNaNNaNNaNNaNNaNSonoraNaNRepatriado de no reincidenciaNaNHombreNaNOaxacaSecundaria CompletaMayor de 18 aƱos no acompaƱado1 a 6 mesesArizonaICEOaxacaSƭSƭNoNoNoNoNoSƭSƭNoSƭNoNoNoNoNaNNaNNaNNaNNaNNaNNaNOaxaca de JuƔrez, Oax.
66262016-08-20116000019181285314328280220000000110100000.0NaNNaNNaNNaNNaNNaNSonoraNaNRepatriado de no reincidenciaNaNHombreNaNTamaulipasSecundaria CompletaMayor de 18 aƱos no acompaƱadoDe 0 a 30 dƭasArizonaICETamaulipasNoNoNoNoNoNoNoSƭSƭNoSƭNoNoNoNoNaNNaNNaNNaNNaNNaNNaNMatamoros, Tamps.
77262016-04-28116000024179853243880191000000110110000.0NaNNaNNaNNaNNaNNaNSonoraNaNRepatriado de no reincidenciaNaNHombreNaNChihuahuaSecundaria CompletaMayor de 18 aƱos no acompaƱado1 a 6 mesesArizonaICEChihuahuaSƭNoNoNoNoNoNoSƭSƭNoSƭSƭNoNoNoNaNNaNNaNNaNNaNNaNNaNChihuahua, Chih.
88262016-03-02116000037176251314225250071000000110101000.0NaNNaNNaNNaNNaNNaNSonoraNaNRepatriado de no reincidenciaNaNHombreNaNSinaloaSin EscolaridadMayor de 18 aƱos no acompaƱadoDe 0 a 30 dƭasArizonaCBPSinaloaSƭNoNoNoNoNoNoSƭSƭNoSƭNoSƭNoNoNaNNaNNaNNaNNaNNaNNaNChoix, Sin.
99262016-03-27116000044176202314220200371100000110101000.0NaNNaNNaNNaNNaNNaNSonoraNaNRepatriado de no reincidenciaNaNHombreNaNOaxacaPrimaria IncompletaMayor de 18 aƱos no acompaƱadoDe 0 a 30 dƭasArizonaCBPOaxacaSƭSƭNoNoNoNoNoSƭSƭNoSƭNoSƭNoNoNaNNaNNaNNaNNaNNaNNaNMesones Hidalgo, Oax.

Last rows

Unnamed: 0DELFEC_REPCLASIF_REPIRESEXOEDAEN_NACNIV_ESCACOM_REPPERM_EUEDO_DETAUT_DEPEN_DESMUN_DESCAN_ALCAN_COMCAN_DIFCAN_HOSCAN_STRACAN_SEGPOPCAN_OFAMAGUA_ALDESC_BUSAPO_AUXAPO_LLAMAPO_MATAPO_TRASAPO_VESAPO_ACT_NACAPO_TRASFAPO_CURPAPO_ASFAPO_AMEAPO_REC_PERTAPO_AT_MEDICAMUN_NACDEL_parsedFEC_REP_parsedCLASIF_REP_parsedIRE_parsedSEXO_parsedEDA_parsedEN_NAC_parsedNIV_ESC_parsedACOM_REP_parsedPERM_EU_parsedEDO_DET_parsedAUT_DEP_parsedEN_DES_parsedCAN_AL_parsedCAN_COM_parsedCAN_DIF_parsedCAN_HOS_parsedCAN_STRA_parsedCAN_SEGPOP_parsedCAN_OFAM_parsedAGUA_AL_parsedDESC_BUS_parsedAPO_AUX_parsedAPO_LLAM_parsedAPO_MAT_parsedAPO_TRAS_parsedAPO_VES_parsedAPO_ACT_NAC_parsedAPO_TRASF_parsedAPO_CURP_parsedAPO_ASF_parsedAPO_AME_parsedAPO_REC_PERT_parsedAPO_AT_MEDICA_parsedMUN_NAC_parsedMUN_DES_parsed
301213301213262021-12-313LOLJOSR113353292800700000011673214279999110000010000000NaN0.00.00.00.00.09999.0SonoraNaNReincidencias posteriores a la primeraNaNHombreNaNChiapasPrimaria CompletaNNA* acompaƱado de un familiarDe 0 a 30 dƭasArizonaCBPChiapasSƭSƭNoNoNoNoNoSƭNoNoNoNoNoNoNoNaNNoNoNoNoNoNo especificadoNo especificado
301214301214262021-12-311GURJOS112162960000120000001531253142129999110000010000000NaN0.00.00.00.00.012029.0SonoraNaNRepatriado de no reincidenciaNaNHombreNaNGuerreroSecundaria CompletaMayor de 18 aƱos no acompaƱadoDe 0 a 30 dƭasArizonaCBPGuerreroSƭSƭNoNoNoNoNoSƭNoNoNoNoNoNoNoNaNNoNoNoNoNoChilpancingo de los Bravo, Gro.No especificado
301215301215262021-12-313PEBFER112704428800150000001361593142159999110000010000000NaN0.00.00.00.00.015033.0SonoraNaNReincidencias posteriores a la primeraNaNHombreNaNMƩxicoLicenciatura CompletaMayor de 18 aƱos no acompaƱadoDe 0 a 30 dƭasArizonaCBPMƩxicoSƭSƭNoNoNoNoNoSƭNoNoNoNoNoNoNoNaNNoNoNoNoNoEcatepec de Morelos, Mex.No especificado
301216301216262021-12-311SAGJOSL11277363520021000001342133142219999110000010000000NaN0.00.00.00.00.021085.0SonoraNaNRepatriado de no reincidenciaNaNHombreNaNPueblaPrimaria CompletaMayor de 18 años no acompañadoDe 0 a 30 díasArizonaCBPPueblaSíSíNoNoNoNoNoSíNoNoNoNoNoNoNoNaNNoNoNoNoNoIzúcar de Matamoros, Pue.No especificado
301217301217262021-12-311HEVTRI112958704000210000001282133142219999110000010000000NaN0.00.00.00.00.021069.0SonoraNaNRepatriado de no reincidenciaNaNHombreNaNPueblaPrimaria CompletaMayor de 18 aƱos no acompaƱadoDe 0 a 30 dƭasArizonaCBPPueblaSƭSƭNoNoNoNoNoSƭNoNoNoNoNoNoNoNaNNoNoNoNoNoHuaquechula, Pue.No especificado
301218301218262021-12-311VAOALF112707366400150000001361533142159999110000010000000NaN0.00.00.00.00.015058.0SonoraNaNRepatriado de no reincidenciaNaNHombreNaNMéxicoPrimaria CompletaMayor de 18 años no acompañadoDe 0 a 30 díasArizonaCBPMéxicoSíSíNoNoNoNoNoSíNoNoNoNoNoNoNoNaNNoNoNoNoNoNezahualcóyotl, Mex.No especificado
301219301219262021-12-311VEHERIA11306488960011000001251173142119999110000010000000NaN0.00.00.00.00.011023.0SonoraNaNRepatriado de no reincidenciaNaNHombreNaNGuanajuatoPreparatoria o bachillerato CompletoMayor de 18 aƱos no acompaƱadoDe 0 a 30 dƭasArizonaCBPGuanajuatoSƭSƭNoNoNoNoNoSƭNoNoNoNoNoNoNoNaNNoNoNoNoNoPƩnjamo, Gto.No especificado
301220301220262021-12-311CUCJUAE11306704960011000001251133142119999110000010000000NaN0.00.00.00.00.011001.0SonoraNaNRepatriado de no reincidenciaNaNHombreNaNGuanajuatoPrimaria CompletaMayor de 18 aƱos no acompaƱadoDe 0 a 30 dƭasArizonaCBPGuanajuatoSƭSƭNoNoNoNoNoSƭNoNoNoNoNoNoNoNaNNoNoNoNoNoAbasolo, Gto.No especificado
301221301221262021-12-311ZAQREB213063161600200000002252054142209999110000010000000NaN0.00.00.00.00.020526.0SonoraNaNRepatriado de no reincidenciaNaNMujerNaNOaxacaSecundaria CompletaMayor de 18 aƱos acompaƱado de un familiarDe 0 a 30 dƭasArizonaCBPOaxacaSƭSƭNoNoNoNoNoSƭNoNoNoNoNoNoNoNaNNoNoNoNoNoSantos Reyes Nopala, Oax.No especificado
301222301222262021-12-311ZAQITA213301712000200000002172052142209999110000010000000NaN0.00.00.00.00.020526.0SonoraNaNRepatriado de no reincidenciaNaNMujerNaNOaxacaSecundaria CompletaNNA* acompaƱado de un familiarDe 0 a 30 dƭasArizonaCBPOaxacaSƭSƭNoNoNoNoNoSƭNoNoNoNoNoNoNoNaNNoNoNoNoNoSantos Reyes Nopala, Oax.No especificado